Dimensionality Reduction with Unsupervised Nearest Neighbors (Hardcover, 2013 ed.)

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:	May 2013
First published:	2013
Authors:	Oliver Kramer
Dimensions:	235 x 155 x 13mm (L x W x T)
Format:	Hardcover
Pages:	132
Edition:	2013 ed.
ISBN-13:	978-3-642-38651-0
Categories:	Books > Business & Economics > Business & management > Management & management techniques > Operational research Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-642-38651-2
Barcode:	9783642386510

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