Produktbild: Dimensionality Reduction with Unsupervised Nearest Neighbors

Dimensionality Reduction with Unsupervised Nearest Neighbors

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Intelligent Systems Reference Library 51

ISBN: 3662518953
ISBN 13: 9783662518953
Autor: Kramer, Oliver
Verlag: Springer Verlag GmbH
Umfang: xii, 132 S., 3 s/w Illustr., 45 farbige Illustr., 132 p. 48 illus., 45 illus. in color.
Erscheinungsdatum: 30.04.2017
Auflage: 1/2013
Produktform: Kartoniert
Einband: Kartoniert

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. 

Artikelnummer: 2157523 Kategorie:

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E-Mail: juergen.hartmann@springer.com

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