Dimensionality Reduction with Unsupervised Nearest Neighbors

TRAMA

This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results. 

SOMMARIO

Part I Foundations.- Part II Unsupervised Nearest Neighbors.- Part III Conclusions.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783662518953
• Collana: Intelligent Systems Reference Library
• Dimensioni: 235 x 155 mm Ø 454 gr
• Formato: Brossura
• Illustration Notes: XII, 132 p. 48 illus., 45 illus. in color.
• Pagine Arabe: 132
• Pagine Romane: xii