Dimensionality Reduction with Unsupervised Nearest Neighbors (e-bog) af Kramer, Oliver
Kramer, Oliver (forfatter)

Dimensionality Reduction with Unsupervised Nearest Neighbors e-bog

875,33 DKK (inkl. moms 1094,16 DKK)
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 reduc...
E-bog 875,33 DKK
Forfattere Kramer, Oliver (forfatter)
Forlag Springer
Udgivet 30 maj 2013
Genrer Management decision making
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9783642386527
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.