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. Â
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