Dimensionality Reduction with Unsupervised Nearest Neighbors

von Oliver Kramer

132 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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. 

• Reihe: Intelligent Systems Reference Library
• Themen: Technologie, Ingenieurswissenschaft, Landwirtschaft, Industrieprozesse Technologie, allgemein Mathematik für Ingenieure
• ISBN: 9783662518953
• Sprache: Englisch
• Erscheinungsdatum: 30.04.2017
• Größe: 23.5 x 15.5 cm
• Verlag: Springer Berlin