Kniha Dimensionality Reduction with Unsupervised Nearest Neighbors Oliver Kramer

Dimensionality Reduction with Unsupervised Nearest Neighbors

Autor: Oliver Kramer
Jazyk: Angličtina
Vazba: Pevná
Dostupnost: Skladem u dodavatele
Odesíláme za 10-13 dnů
2 286
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest ne...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Pevná
Vydáno
2013
Stránek
132
EAN
9783642386510
ISBN
3642386512
Enbook ID
01663789
Hmotnost
3495
Rozměry
155 x 235 x 16

Kompletní popis

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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