Dimensionality reduction with unsupervised nearest neighbors:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg [u.a.]
Springer
2013
|
| Series: | Intelligent systems reference library
51 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben. - Hergestellt on demand |
| Physical Description: | XVIII, 130 S. Ill., graf. Darst. |
| ISBN: | 9783642386510 3642386512 9783642386527 |
Staff View
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LIST OF FIGURES XIII
LIST OF TABLES XVII
1 INTRODUCTION 1
1.1 ARTIFICIAL INTELLIGENCE 1
1.2 MACHINE LEARNING 2
1.3 SUPERVISED LEARNING 3
1.4 UNSUPERVISED LEARNING 5
1.5 MONITORING OF WIND TIME SERIES 5
1.6 GESTURE RECOGNITION 6
1.7 OVERVIEW AND PRELIMINARY PUBLICATIONS 9
PART I: FOUNDATIONS
2 K-NEAREST NEIGHBORS 13
2.1 INTRODUCTION 13
2.2 CLASSIFICATION 13
2.2.1 KNN CLASSIFIER 14
2.2.2 MULTI-CLASS K-NEAREST NEIGHBORS 15
2.3 KNN REGRESSION 15
2.4 STATISTICAL LEARNING THEORY 16
2.5 CROSS-VALIDATION 18
2.6 CURSE OF DIMENSIONALITY 19
2.7 NEAREST NEIGHBOR QUERIES 19
2.8 NEAREST NEIGHBOR VARIANTS 20
2.8.1 MODEL-BASED KNN 21
2.8.2 DISTANCE-WEIGHTED KNN 21
2.8.3 PROPAGATING 1-NEAREST NEIGHBOR 22
2.9 CONCLUSIONS 22
HTTP://D-NB.INFO/1034226894
X CONTENTS
3 ENSEMBLE LEARNING 25
3.1 INTRODUCTION 25
3.2 ENSEMBLES 25
3.3 SUPPORT VECTOR MACHINES 26
3.4 KNN-SVM-ENSEMBLE 27
3.5 RECOGNITION OF APPLIANCES 28
3.5.1 NONINTRUSIVE APPLIANCE LOAD MONITORING 28
3.5.2 FEATURE COMPUTATION 29
3.6 EXPERIMENTAL ANALYSIS OF SVM-KNN-ENSEMBLE 29
3.6.1 INSTALLATION DATA 30
3.6.2 FIELD STUDY DATA 30
3.6.3 NEIGHBORHOOD SIZES OF KNN 31
3.7 CONCLUSIONS 32
4 DIMENSIONALITY REDUCTION 33
4.1 INTRODUCTION 33
4.2 FEATURE SELECTION AND EXTRACTION 34
4.3 CLUSTERING WITH K-MEANS 34
4.4 SELF-ORGANIZING MAPS 36
4.5 PRINCIPAL COMPONENT ANALYSIS 39
4.6 ISOMETRIC MAPPING 40
4.7 LOCALLY LINEAR EMBEDDING 41
4.8 UNSUPERVISED REGRESSION 41
4.9 UNSUPERVISED KERNEL REGRESSION 43
4.9.1 UNSUPERVISED REGRESSION WITH
NADARAYA-WATSON 43
4.9.2 KERNEL DENSITY FUNCTIONS 44
4.9.3 KERNEL BANDWIDTHS 45
4.9.4 UKR OPTIMIZATION 46
4.9.5 ALGORITHMIC VARIANTS 47
4.9.6 FURTHER REGRESSION METHODS 48
4.10 QUALITY MEASURES FOR LATENT EMBEDDINGS 49
4.10.1 QUANTIZATION ERROR 50
4.10.2 TOPOGRAPHIC ERROR 50
4.10.3 CO-RANKING MATRIX 50
4.11 CONCLUSIONS 52
PART II: UNSUPERVISED NEAREST NEIGHBORS
5 LATENT SORTING 55
5.1 INTRODUCTION 55
5.2 UNSUPERVISED NEAREST NEIGHBORS 56
5.2.1 ITERATIVE UNSUPERVISED REGRESSION 56
5.2.2 LATENT SORTING 57
CONTENTS XI
5.3 EXPERIMENTAL ANALYSIS OF UNN 59
5.3.1 5 AND USPS 60
5.3.2 DSRE COMPARISON 61
5.3.3 SORTING OF GALAXIES 61
5.4 ROBUST E-INSENSITIVE LOSS 63
5.5 EXPERIMENTAL ANALYSIS OF ROBUST LOSS FUNCTIONS 64
5.5.1 3D-S 64
5.5.2 HANDWRITTEN DIGITS 66
5.6 MISSING DATA 67
5.7 REPAIR-AND-EMBED 68
5.8 EMBED-AND-REPAIR 69
5.9 EXPERIMENTAL ANALYSIS OF MISSING DATA METHODS 71
5.10 CONCLUSIONS 73
6 METAHEURISTICS 75
6.1 INTRODUCTION 75
6.2 EVOLUTIONARY ALGORITHMS 76
6.2.1 RECOMBINATION 76
6.2.2 MUTATION 77
6.2.3 SELECTION 78
6.2.4 CMA-ES 78
6.3 COMBINATORIAL PERSPECTIVE 79
6.4 CONTINUOUS PERSPECTIVE 81
6.4.1 RESTRICTION OF LATENT SPACE 81
6.4.2 PENALIZING EXTENSION 81
6.5 EXPERIMENTAL ANALYSIS OF EVOLUTIONARY VARIANTS 82
6.5.1 COMPARISON 82
6.5.2 CURSE OF DIMENSIONALITY 83
6.6 SWARM INTELLIGENCE 84
6.6.1 PARTICLE SWARM OPTIMIZATION 85
6.6.2 SWARM-BASED DIMENSIONALITY REDUCTION 86
6.7 ITERATIVE PARTICLE SWARM EMBEDDINGS 86
6.7.1 PSEA APPROACH 86
6.7.2 RUNTIME 87
6.8 EXPERIMENTAL ANALYSIS OF PSEA 88
6.8.1 NEIGHBORHOOD SIZES 88
6.8.2 VISUAL COMPARISON OF EMBEDDINGS 89
6.9 CONCLUSIONS 91
7 KERNEL AND SUBMANIFOLD LEARNING 93
7.1 GAUSSIAN EMBEDDINGS 93
7.2 KERNEL UNN 96
7.2.1 KERNEL FUNCTIONS 96
7.2.2 KERNELIZATION OF DSRE 97
7.3 EXPERIMENTAL ANALYSIS OF KUNN 97
XII CONTENTS
7.3.1 RBF-KERNEL 97
7.3.2 KERNEL FUNCTION COMPARISON 99
7.3.3 ANALYSIS OF LATENT SPACE DIMENSIONALITY 99
7.3.4 COMPARISON BETWEEN KUNN, LLE
AND ISOMAP 100
7.4 MANIFOLD CLUSTERING 102
7.4.1 PROBLEM DEFINITION 102
7.4.2 MANIFOLD CLUSTERING APPROACHES 103
7.5 CONSTRUCTIVE K-MEANS 104
7.6 SUBMANIFOLD LEARNING UNN 106
7.7 EXPERIMENTAL ANALYSIS OF SL-UNN 107
7.7.1 DIGITS 107
7.7.2 DSRE IN SUBMANIFOLDS 108
7.7.3 ISOMAP-FACES 110
7.8 CONCLUSIONS ILL
PART III: CONCLUSIONS
8 SUMMARY AND OUTLOOK 115
8.1 SUMMARY 115
8.1.1 FROM NEAREST NEIGHBORS TO DIMENSIONALITY
REDUCTION 115
8.1.2 FROM LATENT SORTING TO CONTINUOUS
LATENT SPACES 116
8.1.3 KERNEL AND SUBMANIFOLD LEARNING 116
8.2 OUTLOOK 116
8.3 CHALLENGES IN DIMENSIONALITY REDUCTION 118
A TEST PROBLEMS 119
A.L ASTRONOMY 119
A.2 BOSTON 119
A.3 DIGITS 120
A.4 NIALM DATA 120
A. 5 ISOMAP-FACES 120
A.6 S-STRUCTURE 121
REFERENCES 123
INDEX 129 |
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| dewey-search | 510 004 519.5 006.312 |
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| dewey-tens | 510 - Mathematics 000 - Computer science, information, general works |
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| spelling | Kramer, Oliver Verfasser aut Dimensionality reduction with unsupervised nearest neighbors Oliver Kramer Berlin ; Heidelberg [u.a.] Springer 2013 XVIII, 130 S. Ill., graf. Darst. txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 51 Literaturangaben. - Hergestellt on demand Dimensionsreduktion (DE-588)4224279-4 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Nächste-Nachbarn-Problem (DE-588)4376579-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Dimensionsreduktion (DE-588)4224279-4 s Nächste-Nachbarn-Problem (DE-588)4376579-8 s Data Mining (DE-588)4428654-5 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe Kramer, Oliver Dimensionality Reduction with Unsupervised Nearest Neighbors Intelligent systems reference library 51 (DE-604)BV035704685 51 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4314595&prov=M&dok%5Fvar=1&dok%5Fext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027226554&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kramer, Oliver Dimensionality reduction with unsupervised nearest neighbors Intelligent systems reference library Dimensionsreduktion (DE-588)4224279-4 gnd Data Mining (DE-588)4428654-5 gnd Nächste-Nachbarn-Problem (DE-588)4376579-8 gnd Datenanalyse (DE-588)4123037-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4224279-4 (DE-588)4428654-5 (DE-588)4376579-8 (DE-588)4123037-1 (DE-588)4193754-5 |
| title | Dimensionality reduction with unsupervised nearest neighbors |
| title_auth | Dimensionality reduction with unsupervised nearest neighbors |
| title_exact_search | Dimensionality reduction with unsupervised nearest neighbors |
| title_full | Dimensionality reduction with unsupervised nearest neighbors Oliver Kramer |
| title_fullStr | Dimensionality reduction with unsupervised nearest neighbors Oliver Kramer |
| title_full_unstemmed | Dimensionality reduction with unsupervised nearest neighbors Oliver Kramer |
| title_short | Dimensionality reduction with unsupervised nearest neighbors |
| title_sort | dimensionality reduction with unsupervised nearest neighbors |
| topic | Dimensionsreduktion (DE-588)4224279-4 gnd Data Mining (DE-588)4428654-5 gnd Nächste-Nachbarn-Problem (DE-588)4376579-8 gnd Datenanalyse (DE-588)4123037-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Dimensionsreduktion Data Mining Nächste-Nachbarn-Problem Datenanalyse Maschinelles Lernen |
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| work_keys_str_mv | AT krameroliver dimensionalityreductionwithunsupervisednearestneighbors |