Overview
- Presents recent research in the Hybridization of Metaheuristics for Optimization Problems
- State-of-the-Art book
- Written from a leading expert in this field
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 51)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
-
Front Matter
-
Foundations
-
Front Matter
-
-
Unsupervised Nearest Neighbors
-
Front Matter
-
-
Conclusions
-
Front Matter
-
-
Back Matter
Keywords
About this book
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.
Reviews
From the reviews:
“The book provides an overview of the author’s work on dimensionality reduction using unsupervised nearest neighbors. … this book is primarily of interest to scholars who want to learn more about Prof. Kramer’s research on dimensionality reduction.” (Laurens van der Maaten, zbMATH, Vol. 1283, 2014)
Authors and Affiliations
Bibliographic Information
Book Title: Dimensionality Reduction with Unsupervised Nearest Neighbors
Authors: Oliver Kramer
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-642-38652-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-38651-0Published: 11 June 2013
Softcover ISBN: 978-3-662-51895-3Published: 30 April 2017
eBook ISBN: 978-3-642-38652-7Published: 30 May 2013
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XII, 132
Number of Illustrations: 3 b/w illustrations, 45 illustrations in colour
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Operations Research/Decision Theory