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|a 9783642386527
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|a 10.1007/978-3-642-38652-7
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|a Kramer, Oliver.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Dimensionality Reduction with Unsupervised Nearest Neighbors
|h [electronic resource] /
|c by Oliver Kramer.
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|a 1st ed. 2013.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2013.
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|a XII, 132 p. 48 illus., 45 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Intelligent Systems Reference Library,
|x 1868-4394 ;
|v 51
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|a Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions.
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|a 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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|a Applied mathematics.
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|a Engineering mathematics.
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|a Artificial intelligence.
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|a Operations research.
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|a Decision making.
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|a Mathematical and Computational Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11006
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|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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|a Operations Research/Decision Theory.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/521000
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783642386510
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|i Printed edition:
|z 9783642386534
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|i Printed edition:
|z 9783662518953
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|a Intelligent Systems Reference Library,
|x 1868-4394 ;
|v 51
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|u https://doi.org/10.1007/978-3-642-38652-7
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