Dimensionality Reduction with Unsupervised Nearest Neighbors (Paperback, Softcover reprint of the original 1st ed. 2013)

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.  

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Intelligent Systems Reference Library, 51
Release date:	April 2017
First published:	2013
Authors:	Oliver Kramer
Dimensions:	235 x 155 x 8mm (L x W x T)
Format:	Paperback
Pages:	132
Edition:	Softcover reprint of the original 1st ed. 2013
ISBN-13:	978-3-662-51895-3
Categories:	Books > Business & Economics > General Books > Computing & IT > General Books > Professional & Technical > Technology: general issues > General Promotions
LSN:	3-662-51895-3
Barcode:	9783662518953

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