Dimensionality Reduction with Unsupervised Nearest Neighbors

di Oliver Kramer (Autore)

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 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.  

• Autore: Oliver Kramer
• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Collana: Intelligent Systems Reference Library
• Anno: 2013
• Rilegatura: Hardback
• Pagine: 132 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783642386510

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