Dimensionality reduction with unsupervised nearest neighbors /
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, unsuperv...
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| Päätekijä: | |
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| Yhteisötekijä: | |
| Aineistotyyppi: | E-kirja |
| Kieli: | English |
| Julkaistu: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2013.
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| Sarja: | Intelligent Systems Reference Library
51 |
| Aiheet: | |
| Linkit: | Click here to view the full text content |
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| Yhteenveto: | 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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| Ulkoasu: | 1 online resource (XII, 132 pages) 48 illustration., 45 illustration. in colour. digital |
| ISBN: | 9783642386527 |
| ISSN: | 1868-4394 |