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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

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Table of Contents

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    Book Overview
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    Chapter 1 Look and Feel What and How Recurrent Self-Organizing Maps Learn
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    Chapter 2 Self-Organizing Mappings on the Flag Manifold
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    Chapter 3 Self-Organizing Maps with Convolutional Layers
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    Chapter 4 Cellular Self-Organising Maps - CSOM
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    Chapter 5 A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering
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    Chapter 6 SOM-Based Anomaly Detection and Localization for Space Subsystems
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    Chapter 7 Self-Organizing Maps in Earth Observation Data Cubes Analysis
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    Chapter 8 Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps
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    Chapter 9 Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms
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    Chapter 10 Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features
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    Chapter 11 Robust Adaptive SOMs Challenges in a Varied Datasets Analytics
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    Chapter 12 Detection of Abnormal Flights Using Fickle Instances in SOM Maps
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    Chapter 13 LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison
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    Chapter 14 When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns
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    Chapter 15 Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors
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    Chapter 16 A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data
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    Chapter 17 Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm
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    Chapter 18 Investigation of Activation Functions for Generalized Learning Vector Quantization
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    Chapter 19 Robustness of Generalized Learning Vector Quantization Models Against Adversarial Attacks
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    Chapter 20 Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization
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    Chapter 21 Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework
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    Chapter 22 Soft Subspace Topological Clustering over Evolving Data Stream
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    Chapter 23 Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention
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    Chapter 24 Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers
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    Chapter 25 Subspace Quantization on the Grassmannian
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    Chapter 26 Variants of Fuzzy Neural Gas
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    Chapter 27 Autoencoders Covering Space as a Life-Long Classifier
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    Chapter 28 Progressive Clustering and Characterization of Increasingly Higher Dimensional Datasets with Living Self-organizing Maps
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    Chapter 29 A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI
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    Chapter 30 Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer
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    Chapter 31 Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets
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    Chapter 32 Searching for the Origins of Life – Detecting RNA Life Signatures Using Learning Vector Quantization
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    Chapter 33 Simultaneous Display of Front and Back Sides of Spherical SOM for Health Data Analysis
Attention for Chapter 16: A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data
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Chapter title
A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data
Chapter number 16
Book title
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
Published by
Springer, Cham, June 2019
DOI 10.1007/978-3-030-19642-4_16
Book ISBNs
978-3-03-019641-7, 978-3-03-019642-4
Authors

Henry Kvinge, Michael Kirby, Chris Peterson, Chad Eitel, Tod Clapp, Kvinge, Henry, Kirby, Michael, Peterson, Chris, Eitel, Chad, Clapp, Tod

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 63%
Professor 1 13%
Unknown 2 25%
Readers by discipline Count As %
Social Sciences 2 25%
Earth and Planetary Sciences 1 13%
Mathematics 1 13%
Materials Science 1 13%
Engineering 1 13%
Other 0 0%
Unknown 2 25%