Dimensionality Reduction with Unsupervised Nearest Neighbors (Intelligent Systems Reference Library)

Kramer, Oliver

Springer-Verlag Berlin and Heidelberg GmbH & Co. K（2017/04発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 132 p.
• 商品コード 9783662518953

Full Description

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.

 

Contents

Part I Foundations.-

Part II Unsupervised Nearest Neighbors.-

Part III Conclusions.