Dimensionality Reduction with Unsupervised Nearest Neighbors

Name: Dimensionality Reduction with Unsupervised Nearest Neighbors
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783642386510
Price: 428.22 PLN
Availability: InStock

Autor: Oliver Kramer

Język:

Oprawa: Twarda

Wydawca: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Dostępność: Dostępna u dostawcy

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This book is devoted to a novel approach for dimensionality reduction based on the famous nearest ne...

Informacje o książce

Autor

Oliver Kramer

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2013

strony

132

EAN

9783642386510

ISBN

3642386512

Enbook ID

01663789

Wydawca

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Waga

3495

Wymiary

155 x 235 x 16

Pełny opis

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-word 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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