Dimensionality Reduction with Unsupervised Nearest Neighbors [electronic resource] / by Oliver Kramer.
Material type: TextSeries: Intelligent Systems Reference Library ; 51Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XII, 132 p. 48 illus., 45 illus. in color. online resourceContent type:- text
- computer
- online resource
- 9783642386527
- 519 23
- TA329-348
- TA640-643
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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