Dimensionality Reduction with Unsupervised Nearest Neighbors
By: by Oliver Kramer
Contributors: SpringerLink (Online service)
Series: Intelligent Systems Reference Library -- 51
Publisher: Springer Berlin Heidelberg; Imprint: Springer
Published: 2013
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] /
Number of pages: 132
ISBN-13: 9783642386527
Subjects: Appl.Mathematics/Computational Methods of Engineering; Engineering; Operation Research/Decision Theory; Operations research; Engineering mathematics; Artificial intelligence; Artificial Intelligence (incl. Robotics)
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.
Table of contents: title: Part I Foundations; title: Part II Unsupervised Nearest Neighbors; title: Part III Conclusions.