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Unsupervised learning : a dynamic approach / Matthew Kyan, Paisarn Muneesawang, Kambiz Jarrah, Ling Guan.

By: Kyan, Matthew [author.].
Contributor(s): Guan, Ling [author.] | Muneesawang, Paisarn [author.] | Jarrah, Kambiz [author.] | IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type: materialTypeLabelBookSeries: IEEE series on computational intelligence: Publisher: Hoboken, New Jersey : John Wiley & Sons Inc., [2014]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2014]Description: 1 PDF (288 pages).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781118875568.Subject(s): Database management | Self-organizing systems | Machine learning | Big data | Aerospace electronics | Biological cells | Biology | Biomedical imaging | Data mining | Data models | Data visualization | Equations | Euclidean distance | Feature extraction | Filtering theory | Gain control | Histograms | Image analysis | Image coding | Image color analysis | Image segmentation | Indexing | Information filters | Materials | Maximum likelihood detection | Microscopy | Multimedia communication | Network topology | Neurons | Noise | Nonlinear filters | Optical microscopy | Plastics | Probes | Prototypes | Sections | Subspace constraints | Support vector machine classification | Topology | Unsupervised learning | Vectors | Visualization | Wavelet transformsGenre/Form: Electronic books.Additional physical formats: Print version:: No titleOnline resources: Abstract with links to resource Also available in print.
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 -- 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 86 -- 5.5 Detection-Guided Restoration and Real-Time Processing 99 -- 5.5.1 Introduction 99 -- 5.5.2 Iterative Filtering 101 -- 5.5.3 Recursive Filtering 104 -- 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 177 -- 7.3.6 Optional Tuning Stage 177 -- 7.4 Simulations and Evaluation 177 -- 7.4.1 Observations of Evolution and Partitioning 178 -- 7.4.2 Visual Comparisons with Popular Mean-Squared Error Architectures 181 -- 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 -- 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.1 Global and Local Consistency Error 249 -- References 251 -- Index 269.
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Includes bibliographical references and index.

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 -- 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 86 -- 5.5 Detection-Guided Restoration and Real-Time Processing 99 -- 5.5.1 Introduction 99 -- 5.5.2 Iterative Filtering 101 -- 5.5.3 Recursive Filtering 104 -- 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 177 -- 7.3.6 Optional Tuning Stage 177 -- 7.4 Simulations and Evaluation 177 -- 7.4.1 Observations of Evolution and Partitioning 178 -- 7.4.2 Visual Comparisons with Popular Mean-Squared Error Architectures 181 -- 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 -- 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.1 Global and Local Consistency Error 249 -- References 251 -- Index 269.

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