Cart (Loading....) | Create Account
Close category search window
 

An adaptive cluster-target covariance based principal component analysis for interval-valued data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Sato-Ilic, M. ; Fac. of Syst. & Inf. Eng., Univ. of Tsukuba, Tsukuba, Japan

We propose a new principal component analysis (PCA) for interval-valued data by using a covariance involving a fuzzy classification structure based on dissimilarity in higher dimensional space in which objects exist. The covariance for interval-valued data is obtained adaptively by evaluating the validity of the fuzzy classification structure based on the selection of an appropriate number of clusters. In order to select an appropriate number of clusters, we propose an alignment criterion to evaluate the obtained classification structure and prove the concentration of the criterion around the expected value with respect to variation of similarity among clusters. The merit of this PCA is to consider not only the projection of objects to a lower dimensional space, but also the dissimilarity of objects in a higher dimensional space by using a weighted covariance matrix. The weight is estimated as the degree of contribution for the fuzzy classification structure based on dissimilarity of objects in the higher dimensional space. A numerical example of interval-valued data consisting of human based subjective decisions shows a better performance when compared with a result of an ordinary PCA.

Published in:

Fuzzy Systems (FUZZ), 2010 IEEE International Conference on

Date of Conference:

18-23 July 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.