Brain and nature-inspired learning, computation and recognition:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands
Elsevier
2020
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 773 Seiten Illustrationen, Diagramme |
| ISBN: | 9780128197950 |
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| adam_text | Contents Chapter 1: Introduction......................................................................................................... 1 1.1 A brief introduction to the neural network............................................................1 1.1.1 The development of neuralnetworks.................................................................2 1.1.2 Neuron and feedforward neuralnetwork...........................................................3 1.1.3 Backpropagation algorithm......................................... 9 1.1.4 The learning paradigm of neural networks............... 11 1.2 Natural inspired computation............................ 12 1.2.1 Fundamentals of nature-inspired computation..................................... 12 1.2.2 Evolutionary algorithm................................................................................... 12 1.2.3 Artificial immune system (AIS)..................................................................... 15 1.2.4 Other methods................................................................................................. 16 1.3 Machine learning......................................................................................................18 1.3.1 Development of machine learning................................................................. 18 1.3.2 Dimensionality reduction................................................................................20 1.3.3 Sparseness and low-rank......................... 20 1.3.4 Semisupervised
learning................................................................................. 22 1.4 Compressive sensing learning................................. 24 1.4.1 The development of compressive sensing............................. 24 1.4.2 Sparse representation........ ................ 25 1.4.3 Compressive observation................................................................................26 1.4.4 Sparse reconstruction......................................................................................26 1.5 Applications............................................................................. 27 1.5.1 Community detection...................................... 27 1.5.2 Capacitated arc routing optimization.................:..........................................29 1.5.3 Synthetic aperture radar image processing........................................... 32 1.5.4 Hyperspectral image processing............ .........................................................36 References....................................................................................................................... 39 Chapter 2: The models and structure of neural networks............................................47 2.1 Ridgelet neural network..........................................................................................47 2.2 Contourlet neural network......................................................................................50 2.2.1 Nonsubsampled contourlet transforms........................................................... 50 2.2.2 Deep contourlet neural
network..................................................................... 51 2.3 Convolutional neural network................................................................................53
Contents 2.3.1 Convolution...........................................................................................................53 2.3.2 Pooling................................................................................................................... 55 2.3.3 Activation function................................................... 55 2.3.4 Batch normalization............................................................................................. 57 2.3.5 LeNet5................................................................................................................... 58 2.4 Recurrent artificial neural network.............................................................................61 2.5 Generative adversarial nets..........................................................................................64 2.5.1 Biological description—humanbehavior............................................................ 64 2.5.2 Data augmentation............................................................................................... 65 2.5.3 Model description................................................................................................ 65 2.6 Autoencoder................................................................................................................... 68 2.6.1 Layer-wise pretraining.........................................................................................68 2.6.2 Autoencoder network........................................................................................... 69 2.7 Restricted Boltzmann
machine and deep belief network...................................... 73 Further reading..................................................................................................................... 77 Chapter 3: Theoretical basis of natural computation...................................................... 81 3.1 Evolutionary algorithms............................................................................................... 81 3.1.1 Pattern theorem.....................................................................................................81 3.1.2 Implicit parallelism.............................................................................................. 82 3.1.3 Building block assumption..................................................................................83 3.2 Artificial immune system............................................................................................. 83 3.2.1 Markov chain-based convergence analysis........................................................83 3.2.2 Nonlinear dynamic model...................................................................................85 3.3 Multiobjective optimization......................................................................................... 86 3.3.1 Introduction...........................................................................................................86 3.3.2 Mathematical concepts......................................................................................... 88 3.3.3 Multiobjective optimization
algorithms............................................................. 89 References.............................................................................................................................. 94 Chapter 4: Theoretical basis of machine learning............................................................. 97 4.1 Dimensionality reduction............................................................................................. 97 4.1.1 Subspace segmentation........................................................................................ 97 4.1.2 Nonlinear dimensionality reduction................................................................... 98 4.2 Sparseness and low rank............................................................................................ 100 4.2.1 Sparse representation......................................................................................... 100 4.2.2 Matrix recovery and completion.......................................................................100 4.3 Semisupervised learning and kernellearning..........................................................102 4.3.1 Semisupervised learning.................................................................................... 102 4.3.2 Nonparametric kemel learning..........................................................................103 References............................................................................................................................ 104 Chapter 5: Theoretical basis of compressive
sensing......................................................109 5.1 Sparse representation.................................................................................................. 109 5.1.1 Stationary dictionary...........................................................................................Ill vi
Contents 5.1.2 Learning dictionary....................................................................................... 112 5.2 Compressed observation....................................................................................... 113 5.3 Sparse reconstruction............................................................................................ 115 5.3.1 Relaxation methods [56,57].......................................................................... 117 5.3.2 Greedy methods................................................................... 118 5.3.3 Natural computation methods............................................. 120 5.3.4 Other methods............................................................................................... 121 References........................................................................... 122 Chapter 6: Multiobjective evolutionary algorithm (MOEA)-based sparse clustering............................................................................................................................. 127 6.1 Introduction............................................................................................................ 128 6.1.1 The introduction of MOEA on constrained multiobjective optimization problems.................................................................................. 128 6.1.2 An introduction to MOEA on clustering learning and classification learning.......................................................................................................... 129 6.1.3 The introduction of MOEA onsparse
spectral clustering............................ 130 6.2 Modified function and feasible-guiding strategy-based constrained MOPs .... 131 6.2.1 Problem description...................................................................................... 131 6.2.2 Modified objective function........................ 131 6.2.3 The feasible-guiding strategy....................................................................... 134 6.2.4 Procedure for the proposed algorithm...........................................................135 6.3 Learning simultaneous adaptive clustering and classification learning via MOEA.............................................................................................................. 137 6.3.1 Objective functions of MOASCC............................................................. 137 6.3.2 The framework of MOASCC........................................................................139 6.3.3 Computational complexity............................................................................ 143 6.4 A sparse spectral clustering framework via MOEA...........................................143 6.4.1 Mathematical description of SRMOSC.........................................................144 6.4.2 Extension on semisupervised clustering.......................................................145 6.4.3 Initialization................................... ............................................................ 146 6.4.4 Crossover....................................................................................................... 147 6.4.5
Mutation........................................................................................................ 148 6.4.6 Laplacian matrix construction...................................................................... 149 6.4.7 Final solution selection phase...................................................................... 150 6.4.8 Complexity analysis...................................................................................... 150 6.5 Experiments............................................................................................................ 151 6.5.1 The experiments of MOEA on constrained multiobjective optimization problems........................................................................................................ 151 6.5.2 The experiments of MOEA on clustering learning and classification learning.............. 164 6.5.3 The experiments of MOEA onsparse spectral clustering................... 174 6.6 Summary................................................................................................................ 190 References......................................................................................................................191 vii
Contents Chapter 7: MOEA-based community detection........................................ !......... 197 7.1 Introduction................................................................................................. ................198 7.2 Multiobjective community detection based on affinity propagation............ ....200 7.2.1 Background to APMOEA..................................................................................200 7.2.2 Objective functions............................................................................................ 202 7.2.3 The selection method for nondominated solutions.........................................203 7.2.4 Preliminary partition by the AP method..........................................................204 7.2.5 Further search using multiobjective evolutionary algorithm......................... 205 7.2.6 Elitist strategy of the external archive.............................................................208 7.3 Multiobjective community detection based on similarity matrix......................208 7.3.1 Background of GMOEA-net..............................................................................209 7.3.2 Objective functions............................................................................................ 210 7.3.3 The construction of similarity matrix and ¿-nodes update policy................211 7.3.4 Evolutionary operators....................................................................................... 214 7.3.5 The whole framework of GMOEA-
net............................................................216 7.4 Experiments.................................................................................................................216 7.4.1 Evaluation index..................................................................... 216 7.4.2 Networks for simulation....................................................................................218 7.4.3 Comparison algorithms and parameter settings.............................................. 220 7.4.4 Experiments on computer-generated networks............................................... 222 7.4.5 Experiments on real-world networks............................................................... 226 7.5 Summary............................................................ ........................................................228 References.............................. ............................................................................................229 Chapter 8: Evolutionary computation-based multiobjective capacitated arc routing optimizations............ 233 8.1 Introduction..................................................................................................................234 8.2 Multipopulation cooperative coevolutionary algorithm........................................237 8.2.1 Related works......................................................................................................237 8.2.2 Initial population and subpopulations partition.............................................. 240 8.2.3 The fitness evaluation in each
subpopulation................................................. 243 8.2.4 The elitism archiving mechanism.................................................................... 244 8.2.5 The cooperative coevolutionary process..........................................................246 8.2.6 The processing flow of MPCCA...................................................................... 249 8.3 Immune clonal algorithm via directed evolution.................................................. 249 8.3.1 Antibody initialization........................... 251 8.3.2 Immune clonal operation...................................................................................252 8.3.3 Immune gene operations.................................................................................... 253 8.3.4 The processing flow of DE-ICA...................................................................... 258 8.4 Improved memetic algorithm via route distance grouping................................258 8.4.1 Solutions for the timelyreplacement of IRDG-MAENS................................259 8.4.2 Determine the regionswhich individuals belong to....................................... 260 8.4.3 The processing flow of IRDG-MAENS...........................................................263 8.5 Experiments...................................................... 264 viii
Contents 8.5.1 Test problems and experimental setup........................................................ 264 8.5.2 The performance metrics............................................................................. 265 8.5.3 Wilcoxon signed rank test........................................................................... 266 8.5.4 Comparison of the evaluation metrics........................................................ 266 8.5.5 Comparison of nondominant solutions........................................................ 289 8.6 Summary................................................................................................................297 References.................................................................. Chapter 9: Multiobjective optimization algorithm-based image segmentation........ 301 9.1 Introduction.................................. 301 9.2 Multiobjective evolutionary fuzzy clustering with MOEA/D...........................303 9.2.1 Fuzzy-C means clustering algorithms with local information....................303 9.2.2 Framework of MOEFC.................................................................................305 9.2.3 Opposition-based learning operator.............................................................. 308 9.2.4 Mixed population initialization....................................................................309 9.2.5 The time complexity analysis...................................... 310 9.3 Multiobjective immune algorithm for SAR image segmentation....................310 9.3.1 Definitions of AIS-based, multiobjective
optimization................................310 9.3.2 The stage of features extraction and preprocessing......................................312 9.3.3 The immune multiobjective framework for SAR imagery segmentation.... 316 9.4 Experiments............................................................................................................317 9.4.1 The MOEFC experiments............................................................................. 317 9.4.2 The IMIS experiments.................................................................................. 337 9.5 Summary..................................................................................... References....................................................................................................... Chapter 10: Graph-regularized feature selection based on spectral learning and subspace learning..................................................................................... 351 10.1 Nonnegative spectral learning and subspace learning-based graph-regularized feature selection................................................................... 352 10.1.1 Dual-graph nonnegative spectral learning................................................ 352 10.1.2 Dual-graph sparse regression.................................................................... 356 10.1.3 Feature selection........................................................ 357 10.1.4 Optimization............................................................. 358 10.1.5 Local structure
preserving........................................................................ 360 10.1.6 Update rules for SGFS.......................................................... 361 10.2 Experiments of spectral learning and subspace learning methods for feature selection................................................................................. 10.2.1 Experiments and analysis of NSSRD................ 362 10.2.2 Experiments and analysis of SGFS....................................... 368 References.......................................................................................... Chapter 11: Semisupervised learning based on nuclear norm regularization......... 387 11.1 Framework of semisupervised learning (SSL) with nuclear norm regularization....................................................................................................... 387 IX 346 362
Contents 11.1.1 A general framework..................................................................................... 387 11.1.2 Nuclear norm regularized model.................................................................388 11.1.3 Modified fixed point algorithm........... ......................................................... 389 11.1.4 Implementation............................................................................................... 392 11.1.5 Label propagation................................ 393 11.1.6 Valid kernel.....................................................................................................394 11.2 Experiments and analysis........................................................................................395 11.2.1 Compared algorithms and parameter settings............................................. 395 11.2.2 Synthetic data................................................................................................. 397 11.2.3 Real-world data sets....................................................................................... 398 11.2.4 Transduction classification results................................................................ 399 References............................................................................................................................405 Chapter 12: Fast clustering methods based on learning spectral embedding...........407 12.1 Learning spectral embedding for semisupervised clustering.......................... 408 12.1.1 Graph construction and spectral
embedding............................................... 408 12.1.2 Problem formulation.............................. 410 12.1.3 Algorithm........................................................................................................414 12.1.4 Experiments.................................................................................................... 415 12.2 Fast semisupervised clustering with enhanced spectral embedding............... 421 12.2.1 Problem formulation...................................................................................... 421 12.2.2 Algorithm........................................................................................................423 References............................................................................. Chapter 13: Fast clustering methods based on affinity propagation and density weighting......................................................................................................................437 13.1 The framework of fast clustering methods based on affinity propagation and density weighting........................................................................ 438 13.1.1 Related works................................................................................................. 438 13.1.2 Fast AP algorithm.......................................................................................... 441 13.1.3 Fast two-stage spectral clustering framework............................................. 446 13.2 Experiments and
analysis........................................................................................455 13.2.1 Experiments on the method based on affinity propagation....................... 455 13.2.2 Experiments on the method based on density-weighting.......................... 465 References............................................................................................................................474 Chapter 14: SAR image processing based on similarity measures and discriminant feature learning................................................................................................. 477 14.1 SAR image retrieval based on similarity measures............................................478 14.1.1 Semantic classification and region-based similarity measures..................478 14.1.2 Fusion similarity-based reranking for SAR image retrieval.....................494 14.1.3 SAR image content retrieval based on fuzzy similarity and relevance feedback......................................................................................... 502 14.2 SAR image change detection based on spatial coding and similarity............ 528 x
Contents 14.2.1 Saliency-guided change detection for SAR imagery using a semisupervised Laplacian SVM............................................................... 528 14.2.2 SAR images change detection based on spatial coding and nonlocal similarity pooling...................................................................................... 535 References..................................................................................................................... 554 Chapter 15: Hyperspectral image processing based on sparse learning and sparse graph............................................................................................................................. 559 15.1 Hyperspectral image denoising based on hierarchicalsparse learning............560 15.1.1 Spatial-spectral data extraction................................................................. 561 15.1.2 Hierarchical sparse learning for denoisingeach band-subset................... 563 15.1.3 Experimental results and discussion........................................................ 566 15.2 Hyperspectral image restoration based on hierarchical sparse Bayesian learning.................................................................................................................581 15.2.1 Beta process..............................................................................................581 15.2.2 Experimental results..................................................................................584 15.3 Hyperspectral image dimensionality reductionusing asparse graph...............
595 15.3.1 Sparse representation................................................................................595 15.3.2 Sparse graph-based dimensionality reduction..................... 596 15.3.3 Sparse graph learning...............................................................................597 15.3.4 Spatial-spectral clustering......................................................................... 602 15.3.5 Experimental results.................................................................................. 602 References..................................................................................................................... 613 Chapter 16: Nonconvex compressed sensing framework based on block strategy and overcomplete dictionary.................................................................................................. 617 16.1 Introduction..........................................................................................................617 16.2 The block compressed sensing framework based on the overcomplete dictionary..................................................................................................... 618 16.2.1 Block compressed sensing........................................................................ 618 16.2.2 Overcomplete dictionary........................................................................... 619 16.2.3 Structured compressed sensing model..................................................... 620 16.3 Image sparse representation based on the ridgelet overcomplete dictionary................. 620
16.4 Structured reconstruction model........................................................................ 624 16.4.1 Structural sparse prior based on image self-similarity.............................624 16.4.2 Reconstruction model based on an estimation of the direction structure of image blocks......................................................................... 625 16.5 Nonconvex reconstruction strategy.................................................................... 626 References..................................................................................................................... 626 Chapter 17: Sparse representation combined with fuzzy C-means (FCM) in compressed sensing..................................................................................................................629 17.1 Basic introduction to fuzzy C-means (FCM) and sparse representation (SR)............................................................................................. 629 XI
Contents 17.2 Two versions combining FCM with SR............................................ .................. 635 17.2.1 FDCM_SSR.................................................................................................... 635 17.2.2 SL_FCM..................... 639 17.3 Experimental results..................................................................................................644 17.3.1 FDCM_SSR.................................................................................................... 644 17.4 SAR images................................................................................................................654 17.4.1 SL_FCM..................................... 655 References............................................................................................................................665 Chapter 18: Compressed sensing by collaborative reconstruction............................... 669 18.1 Introduction................................................................................................................669 18.2 Methods.......................................... 671 18.2.1 Block CS of images...................................................................... 671 18.2.2 Collaborative reconstruction method based on an overcomplete dictionary............................................ 672 18.2.3 Geometric structure-guided collaborative reconstruction method.............676 18.3 Experiment.................................................... 682 18.3.1 Collaborative reconstruction method based on an overcomplete
dictionary.........................................................................................................682 18.3.2 Geometric structure-guided collaborative reconstruction method.............684 References................................... 691 Chapter 19: Hyperspectral image classification based on spectral information divergence and sparse representation..................................................................................693 19.1 The research status and challenges of hyperspectral image classification.... 693 19.1.1 The research status of hyperspectral image classification........................ 693 19.1.2 The challenges of hyperspectral image classification................................695 19.2 Motivation................................... 696 19.3 Spectral information divergence (SID).................................................................698 19.4 Sparse representation classification method based on SID...............................699 19.5 Joint sparse representation classification method based on SID......................700 19.6 Experimental results and analysis..........................................................................701 19.6.1 Comparison of the measurements................................................................702 19.6.2 Comparison of the performance of sparse representation classification methods....................................................................................703 19.6.3 Analysis of parameters..................................................................................704 19.6.4
The proof of convergence..............................................................................707 References............................................................................................................................707 Chapter 20: Neural network-based synthetic aperture radar image processing..... 709 20.1 Discriminant deep belief network for SAR image classification.....................710 20.1.1 Weak classifiers training................................................................................710 20.1.2 Discriminative projection...............................................................................710 20.1.3 High-level discriminative feature learning.................................................. 711 20.1.4 Experiment and result................................................. 711 xii
Contents 20.2 Convolutional-wavelet neural network for SAR image segmentation.......... 714 20.2.1 Overall framework....................................................................................715 20.2.2 Experiment and result............................................................................... 715 20.3 Deep neural network for SAR image registration.......................................... 715 20.3.1 Train deep neural network........................................................................ 717 20.3.2 Predicting the matching label and eliminate the wrong matching points...................................... 718 20.3.3 Experiment and result............................................................................... 718 References.....................................................................................................................720 Chapter 21: Neural networks-based polarimetrie SAR image classification........... 723 21.1 PolSAR decomposition................................ 724 21.2 Autoencoder for PolSAR image classification..................................................725 21.2.1 Data processing and feature learning....................................................... 725 21.2.2 Experiment and result............................................................................... 726 21.3 DBN for PolSAR image classification.............................................................. 728 21.3.1 DBN structure and feature learning......................................................... 728 21.3.2 Experiment and
result...................................................... 728 21.4 Wishart deep stacking networks for PolSAR image classification............... 730 21.4.1 Wishart distance and network structure................................................... 730 21.4.2 Experiment and results............................................... 731 References................................................................................................. 733 Chapter 22: Deep neural network models for hyperspectral images .......................735 22.1 Deep fully convolutional network..................................................................... 735 22.1.1 Fully convolutional networks...................................... 736 22.1.2 Deep multiscale spatial distribution prediction via FCN-8s....................736 22.1.3 Spatial-spectral feature fusion and classification for HSI.......................737 22.1.4 Experiment and results.............................................................................737 22.2 Recursive autoencoders................................................. 738 22.2.1 Unsupervised RAE....................................................................................740 22.2.2 Experiments and results............................................................................ 740 22.3 Superpixel-based multiple local CNN...................................................... 741 22.3.1 Multiple local regions joint representation CNN model..........................743 22.3.2 Experiments and
results............................................................................745 References........................................................................................................ 747 Index....................................................................................................................................749 xm
|
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| author | Jiao, Li-cheng |
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| id | DE-604.BV046416498 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T11:28:00Z |
| institution | BVB |
| isbn | 9780128197950 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031829015 |
| oclc_num | 1144123452 |
| open_access_boolean | |
| owner | DE-739 DE-862 DE-BY-FWS |
| owner_facet | DE-739 DE-862 DE-BY-FWS |
| physical | XIII, 773 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| record_format | marc |
| spellingShingle | Jiao, Li-cheng Brain and nature-inspired learning, computation and recognition Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4226127-2 |
| title | Brain and nature-inspired learning, computation and recognition |
| title_auth | Brain and nature-inspired learning, computation and recognition |
| title_exact_search | Brain and nature-inspired learning, computation and recognition |
| title_full | Brain and nature-inspired learning, computation and recognition Licheng Jiao, Ronghua Shang, Fang Liu, Weitong Zhang |
| title_fullStr | Brain and nature-inspired learning, computation and recognition Licheng Jiao, Ronghua Shang, Fang Liu, Weitong Zhang |
| title_full_unstemmed | Brain and nature-inspired learning, computation and recognition Licheng Jiao, Ronghua Shang, Fang Liu, Weitong Zhang |
| title_short | Brain and nature-inspired learning, computation and recognition |
| title_sort | brain and nature inspired learning computation and recognition |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Maschinelles Lernen Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031829015&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jiaolicheng brainandnatureinspiredlearningcomputationandrecognition AT shangronghua brainandnatureinspiredlearningcomputationandrecognition AT liufang brainandnatureinspiredlearningcomputationandrecognition AT zhangweitong brainandnatureinspiredlearningcomputationandrecognition |
Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
| Signatur: |
2000 ST 301 J61 |
|---|---|
| Exemplar 1 | ausleihbar Verfügbar Bestellen |