Advances in self-organizing maps and learning vector quantization : Proceedings of the 11th International Workshop, WSOM 2016, Rice University Houston, Texas, USA, January 6-8 2016

"This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data" -- OhioLink Library Catalog Front Matter....Pages i-xiii Front Matter....Pages 1-1 Theoretical and Applied Aspects of the Self-Organizing Maps....Pages 3-26 Aggregating Self-Organizing Maps with Topology Preservation....Pages 27-37 ESOM Visualizations for Quality Assessment in Clustering....Pages 39-48 SOM Quality Measures: An Efficient Statistical Approach....Pages 49-59 SOM Training Optimization Using Triangle Inequality....Pages 61-71 Sparse Online Self-Organizing Maps for Large Relational Data....Pages 73-82 Front Matter....Pages 83-83 A Neural Gas Based Approximate Spectral Clustering Ensemble....Pages 85-93 Reliable Clustering Quality Estimation from Low to High Dimensional Data....Pages 95-105 Segment Growing Neural Gas for Nonlinear Time Series Analysis....Pages 107-117 Modeling Diversity in Ensembles for Time-Series Prediction Based on Self-Organizing Maps....Pages 119-128 Front Matter....Pages 129-129 Modular Self-Organizing Control for Linear and Nonlinear Systems....Pages 131-141 On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning....Pages 143-153 Dimensionality Reduction Hybridizations with Multi-dimensional Scaling....Pages 155-163 A Scalable Flexible SOM NoC-Based Hardware Architecture....Pages 165-175 Local Models for Learning Inverse Kinematics of Redundant Robots: A Performance Comparison....Pages 177-187 Front Matter....Pages 189-189 Using SOMs to Gain Insight into Human Language Processing....Pages 191-191 Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data....Pages 193-203 LVQ and SVM Classification of FDG-PET Brain Data....Pages 205-215 Mutual Connectivity Analysis (MCA) for Nonlinear Functional Connectivity Network Recovery in the Human Brain Using Convergent Cross-Mapping and Non-metric Clustering....Pages 217-226 SOM and LVQ Classification of Endovascular Surgeons Using Motion-Based Metrics....Pages 227-237 Front Matter....Pages 189-189 Visualization and Practical Use of Clinical Survey Medical Examination Results....Pages 239-249 The Effect of SOM Size and Similarity Measure on Identification of Functional and Anatomical Regions in fMRI Data....Pages 251-263 Front Matter....Pages 265-265 Big Data Era Challenges and Opportunities in Astronomy—How SOM/LVQ and Related Learning Methods Can Contribute?....Pages 267-267 Self-Adjusting Reject Options in Prototype Based Classification....Pages 269-279 Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization....Pages 281-291 Complex Variants of GLVQ Based on Wirtinger’s Calculus....Pages 293-303 A Study on GMLVQ Convex and Non-convex Regularization....Pages 305-314 Front Matter....Pages 315-315 Functional Representation of Prototypes in LVQ and Relevance Learning....Pages 317-327 Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis....Pages 329-339 Low-Rank Kernel Space Representations in Prototype Learning....Pages 341-353 Dynamic Prototype Addition in Generalized Learning Vector Quantization....Pages 355-368 Back Matter....Pages 369-370