Dimensionality Reduction with Unsupervised Nearest Neighbors
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 appli
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Oliver Kramer
Dimensionality Reduction with Unsupervised Nearest Neighbors
123
Intelligent Systems Reference Library Volume 51
Series Editors J. Kacprzyk, Warsaw, Poland L. C. Jain, Adelaide, Australia
For further volumes: http://www.springer.com/series/8578
Oliver Kramer
Dimensionality Reduction with Unsupervised Nearest Neighbors
ABC
Oliver Kramer Computational Intelligence Group Computer Science Department Carl von Ossietzky University Oldenburg 26111 Oldenburg Germany
ISSN 1868-4394 ISSN 1868-4408 (electronic) ISBN 978-3-642-38651-0 ISBN 978-3-642-38652-7 (eBook) DOI 10.1007/978-3-642-38652-7 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013939181 c Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Abstract
The growing information infrastructure in a variety of disciplines involves an increasing requirement for efficient data mining techniques. Fast dimensionality reduction methods are important for understanding and processing of large data sets of high-dimensional patterns. In this work, unsupervised nearest neighbors (UNN), an efficient iterative method for dimensionality reduction, is presented. Starting with an introduction to machine learning and dimensionality reductio
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