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Dimensionality Reduction with Unsupervised Nearest Neighbors

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Dimensionality Reduction with Unsupervised Nearest Neighbors
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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.

Informacija

Autorius: Oliver Kramer
Leidėjas: Springer
Išleidimo metai: 2025
Knygos puslapių skaičius: 144
ISBN-13: 9783662518953
Formatas: 6.1 x 0.35 x 9.25 inches. Knyga minkštu viršeliu
Kalba: Anglų

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