Dimensionality reduction with unsupervised nearest neighbors / / Oliver Kramer

• Autore: Kramer Oliver
• Pubblicazione: Dordrecht, : Springer, 2013
• Edizione: 1st ed. 2013.
• Descrizione fisica: 1 online resource (xviii, 130 pages) : illustrations (some color)
• Disciplina: 006.31; 519.5/36
• Soggetto topico: Dimensions; Data mining
• Note generali: "ISSN: 1868-4394."
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions.
• 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. .
• Titolo autorizzato: Dimensionality Reduction with Unsupervised Nearest Neighbors
• ISBN: 3-642-38652-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910438057103321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent systems reference library ; ; v. 51.