Unsupervised learning: a dynamic approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
2014
|
| Schriftenreihe: | IEEE Press series on computational intelligence
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Weitere Ausgabe: Online version : Unsupervised learning |
| Beschreibung: | XI, 273 S. Ill., graf. Darst. |
| ISBN: | 9780470278338 9781118875230 |
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| 300 | |a XI, 273 S. |b Ill., graf. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804152324924375040 |
|---|---|
| adam_text | CONTENTS
Acknowledgments
xi
1
Introduction
1
1.1
Part I: The Self-Organizing Method
1
1.2
Part II: Dynamic Self-Organization for Image Filtering and
Multimedia Retrieval
2
1.3
Part III: Dynamic Self-Organization for Image Segmentation and
Visualization
5
1.4
Future Directions
7
2
Unsupervised Learning
9
2.1
Introduction
9
2.2
Unsupervised Clustering
9
2.3
Distance Metrics for Unsupervised Clustering
11
2.4
Unsupervised Learning Approaches
13
2.4.1
Partitioning and Cluster Membership
13
2.4.2
Iterative Mean-Squared Error Approaches
15
2.4.3
Mixture Decomposition Approaches
17
2.4.4
Agglomerative Hierarchical Approaches
18
2.4.5
Graph-Theoretic Approaches
20
2.4.6
Evolutionary Approaches
20
2.4.7
Neural Network Approaches
21
2.5
Assessing Cluster Quality and Validity
21
2.5.1
Cost Function-Based Cluster Validity Indices
22
2.5.2
Density-Based Cluster Validity Indices
23
2.5.3
Geometric-Based Cluster Validity Indices
24
3
Self-Organization
27
3.1
Introduction
27
3.2
Principles of Self-Organization
27
3.2.1
Synaptic Self-Amplification and Competition
27
3.2.2
Cooperation 28
3.2.3
Knowledge Through Redundancy
29
VÍ
CONTENTS
3.3
Fundamental Architectures 29
3.3.1
Adaptive Resonance Theory
29
3.3.2
Self-Organizing Map
37
3.4
Other Fixed Architectures for Self-Organization
43
3.4.1
Neural Gas
44
3.4.2
Hierarchical Feature Map
45
3.5
Emerging Architectures for Self-Organization
46
3.5.1
Dynamic Hierarchical Architectures
47
3.5.2
Nonstationary Architectures
48
3.5.3
Hybrid Architectures
50
3.6
Conclusion
50
4
Self-Organizing Tree Map
53
4.1
Introduction
53
4.2
Architecture
54
4.3
Competitive Learning
55
4.4
Algorithm
57
4.5
Evolution
61
4.5.1
Dynamic Topology
61
4.5.2
Classification Capability
64
4.6
Practical Considerations, Extensions,
and Refinements
68
4.6.1
The Hierarchical Control Function
68
4.6.2
Learning, Timing, and Convergence
71
4.6.3
Feature Normalization
73
4.6.4
Stop Criteria
73
4.7
Conclusions
74
5
Self-Organization in Impulse Noise Removal
75
5.1
Introduction
75
5.2
Review of Traditional Median-Type Filters
76
5.3
The Noise-Exclusive Adaptive Filtering
82
5.3.1
Feature Selection and Impulse Detection
82
5.3.2
Noise Removal Filters
84
5.4
Experimental Results g6
5.5
Detection-Guided Restoration and
Real-Time
Processing
99
5.5.1
Introduction
99
5.5.2
Iterative Filtering
j q
ţ
5.5.3
Recursive Filtering
j
04
CONTENTS
VU
5.5.4
Real-Time Processing
of
Impulse
Corrupted
TV Pictures
105
5.5.5
Analysis of the Processing Time
109
5.6
Conclusions
115
6
Self-Organization in Image Retrieval
119
6.1
Retrieval of Visual Information
120
6.2
Visual Feature Descriptor
122
6.2.1
Color Histogram and Color Moment Descriptors
122
6.2.2
Wavelet Moment and
Gabor
Texture Descriptors
123
6.2.3
Fourier and Moment-based Shape Descriptors
125
6.2.4
Feature Normalization and Selection
127
6.3
User-Assisted Retrieval
130
6.3.1
Radial Basis Function Method
132
6.4
Self-Organization for
Pseudo
Relevance Feedback
136
6.5
Directed Self-Organization
140
6.5.1
Algorithm
142
6.6
Optimizing Self-Organization for Retrieval
146
6.6.1
Genetic Principles
147
6.6.2
System Architecture
149
6.6.3
Genetic Algorithm for Feature Weight Detection
150
6.7
Retrieval Performance
153
6.7.1
Directed Self-Organization
153
6.7.2
Genetic Algorithm Weight Detection
155
6.8
Summary
157
7
The Self-Organizing Hierarchical Variance Map
159
7.1
An Intuitive Basis
160
7.2
Model Formulation and Breakdown
162
7.2.1
Topology Extraction via Competitive Hebbian Learning
163
7.2.2
Local Variance via Hebbian Maximal
Eigenfilters 165
7.2.3
Global and Local Variance Interplay for Map Growth and
Termination
170
7.3
Algorithm 173
7.3.1
Initialization, Continuation, and Presentation
173
7.3.2
Updating Network Parameters
175
7.3.3
Vigilance Evaluation and Map Growth
175
7.3.4
Topology Adaptation
176
7.3.5
Node Adaptation I77
7.3.6
Optional Tuning Stage
177
Vili
CONTENTS
7.4
Simulations
and
Evaluation I7
7.4.1
Observations of
Evolution
and Partitioning
178
7 4 2 Visual
Comparisons with Popular Mean-Squared Error
Architectures
°
7.4.3
Visual Comparison Against Growing Neural Gas
183
7.4.4
Comparing Hierarchical with Tree-Based Methods
183
7.5
Tests on Self-Determination and the Optional Tuning Stage
187
7.6
Cluster Validity Analysis on Synthetic and UCI Data
187
7.6.1
Performance vs. Popular Clustering Methods
190
7.6.2
IRIS
Dataset
192
7.6.3
WINE
Dataset
195
7.7
Summary 195
8
Microbiological Image Analysis Using Self-Organization
197
8.1
Image Analysis in the
Biosciences
197
8.1.1
Segmentation: The Common Denominator
198
8.1.2
Semi-supervised versus Unsupervised Analysis
199
8.1.3
Confocal Microscopy and Its Modalities
200
8.2
Image Analysis Tasks Considered
202
8.2.1
Visualising Chromosomes During Mitosis
202
8.2.2
Segmenting Heterogeneous
Biofilms
204
8.3
Microbiological Image Segmentation
205
8.3.1
Effects of Feature Space Definition
207
8.3.2
Fixed Weighting of Feature Space
209
8.3.3
Dynamic Feature Fusion During Learning
213
8.4
Image Segmentation Using Hierarchical Self-Organization
215
8.4.1
Gray-Level Segmentation of Chromosomes
215
8.4.2
Automated Multilevel Thresholding of
Biofilm
220
8.4.3
Multidimensional Feature Segmentation
221
8.5
Harvesting Topologies to Facilitate Visualization
226
8.5.1
Topology Aware Opacity and Gray-Level Assignment
227
8.5.2
Visualization of Chromosomes During Mitosis
228
8.6
Summary
233
9
Closing Remarks and Future Directions
237
9.1
Summary of Main Findings
237
9.1.1
Dynamic Self-Organization: Effective Models for Efficient
Feature Space Parsing
237
9.1.2
Improved Stability, Integrity, and Efficiency
238
9.1.3
Adaptive Topologies Promote Consistency and
Uncover Relationships
239
CONTENTS
IX
9.1.4 Online
Selection of Class Number
239
9.1.5
Topologies Represent a Useful Backbone for
Visualization or Analysis
240
9.2
Future Directions
240
9.2.1
Dynamic Navigation for Information Repositories
241
9.2.2
Interactive Knowledge-Assisted Visualization
243
9.2.3
Temporal Data Analysis Using Trajectories
245
Appendix A
249
A.I Global and Local Consistency Error
249
References
251
Index
269
|
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| indexdate | 2024-07-10T01:08:55Z |
| institution | BVB |
| isbn | 9780470278338 9781118875230 |
| language | English |
| lccn | 2013046024 |
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| spelling | Kyan, Matthew Verfasser (DE-588)1053697635 aut Unsupervised learning a dynamic approach Matthew Kyan ... Hoboken, N.J. Wiley 2014 XI, 273 S. Ill., graf. Darst. txt rdacontent n rdamedia nc rdacarrier IEEE Press series on computational intelligence Weitere Ausgabe: Online version : Unsupervised learning Soft Computing (DE-588)4455833-8 gnd rswk-swf Selbstorganisation (DE-588)4126830-1 gnd rswk-swf Soft Computing (DE-588)4455833-8 s Selbstorganisation (DE-588)4126830-1 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027389310&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kyan, Matthew Unsupervised learning a dynamic approach Soft Computing (DE-588)4455833-8 gnd Selbstorganisation (DE-588)4126830-1 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)4126830-1 |
| title | Unsupervised learning a dynamic approach |
| title_auth | Unsupervised learning a dynamic approach |
| title_exact_search | Unsupervised learning a dynamic approach |
| title_full | Unsupervised learning a dynamic approach Matthew Kyan ... |
| title_fullStr | Unsupervised learning a dynamic approach Matthew Kyan ... |
| title_full_unstemmed | Unsupervised learning a dynamic approach Matthew Kyan ... |
| title_short | Unsupervised learning |
| title_sort | unsupervised learning a dynamic approach |
| title_sub | a dynamic approach |
| topic | Soft Computing (DE-588)4455833-8 gnd Selbstorganisation (DE-588)4126830-1 gnd |
| topic_facet | Soft Computing Selbstorganisation |
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