Dimensionality Reduction with Unsupervised Nearest Neighbors

Description

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.

Subjects

Links

• http://dx.doi.org/10.1007/978-3-642-38652-7

Other Editions

• Dimensionality Reduction with Unsupervised Nearest NeighborsElectronic resourceSpringer Berlin Heidelberg2013-01-01
• Dimensionality Reduction with Unsupervised Nearest NeighborsPaperbackSpringer2013-06-04
• Dimensionality Reduction with Unsupervised Nearest NeighborsPaperbackSpringer2016-06-12