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[null,"1.","Record Nr.",null,"UNINA9910438057103321"]
[null,null,"Autore",null,"Kramer Oliver"]
[null,null,"Titolo",null,"Dimensionality reduction with unsupervised nearest neighbors / / Oliver Kramer"]
[null,null,"Pubbl/distr/stampa",null,"Dordrecht, : Springer, 2013"]
[null,null,"ISBN",null,"3-642-38652-0"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xviii, 130 pages) : illustrations (some color)"]
[null,null,"Collana",null,"Intelligent systems reference library ; ; 51"]
[null,null,"Disciplina",null,"006.31"]
[{"$nulls":4},"519.5/36"]
[null,null,"Soggetti",null,"Dimensions"]
[{"$nulls":4},"Data mining"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"ISSN: 1868-4394.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions."]
[null,null,null,"Sommario/riassunto","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.  ."]