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Record Nr. |
UNINA9910438057103321 |
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Autore |
Kramer Oliver |
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Titolo |
Dimensionality Reduction with Unsupervised Nearest Neighbors / / by Oliver Kramer |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (xviii, 130 pages) : illustrations (some color) |
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Collana |
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Intelligent Systems Reference Library, , 1868-4408 ; ; 51 |
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Disciplina |
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Soggetti |
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Engineering mathematics |
Engineering - Data processing |
Artificial intelligence |
Operations research |
Mathematical and Computational Engineering Applications |
Artificial Intelligence |
Operations Research and Decision Theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions. |
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Sommario/riassunto |
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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 |
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