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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.
| Autorius: | Oliver Kramer |
| Serija: | Intelligent Systems Reference Library |
| Leidėjas: | Springer Berlin Heidelberg |
| Išleidimo metai: | 2013 |
| Knygos puslapių skaičius: | 148 |
| ISBN-10: | 3642386512 |
| ISBN-13: | 9783642386510 |
| Formatas: | 241 x 160 x 13 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
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