Dimensionality Reduction with Unsupervised Nearest Neighbors / Intelligent Systems Reference Library Bd.51 (PDF)
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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 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.
- Autor: Oliver Kramer
- 2013, 2013, 132 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3642386520
- ISBN-13: 9783642386527
- Erscheinungsdatum: 30.05.2013
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- Dateiformat: PDF
- Grösse: 8.51 MB
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“The book provides an overview of the author’s work on dimensionality reduction using unsupervised nearest neighbors. … this book is primarily of interest to scholars who want to learn more about Prof. Kramer’s research on dimensionality reduction.” (Laurens van der Maaten, zbMATH, Vol. 1283, 2014)
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