Band 51
Dimensionality Reduction with Unsupervised Nearest Neighbors
Aus der Reihe
Intelligent Systems Reference Library
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 138.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
30.04.2017
Abbildungen
XII, 132 p. 48 illus., 45 illus. in color.
Verlag
Springer BerlinSeitenzahl
132
Maße (B/H)
15.5/23.5 cm
Gewicht
236 g
Auflage
Softcover reprint of the original 1st ed. 2013
Sprache
Englisch
ISBN
978-3-662-51895-3
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.
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