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Dimensionality Reduction with Unsupervised Nearest Neighbors

by Kramer, Oliver.
Authors: SpringerLink (Online service) Series: Intelligent Systems Reference Library, 1868-4394 ; . 51 Physical details: XII, 132 p. 48 illus., 45 illus. in color. online resource. ISBN: 3642386520 Subject(s): Engineering. | Artificial intelligence. | Engineering mathematics. | Operations research. | Engineering. | Appl.Mathematics/Computational Methods of Engineering. | Artificial Intelligence (incl. Robotics). | Operation Research/Decision Theory.
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E-Book E-Book AUM Main Library 519 (Browse Shelf) Not for loan

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

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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