By Topic

Integration of KPCA and parallel coordinates for visualizing classification

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
$33 $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

4 Author(s)
Suwei Wang ; College of Polytechnic, Hunan Normal University, Changsha, China ; Jun Lin ; Junhu Lei ; Jiahong Yang

Combined with pattern recognition and information visualization technology, this paper proposes a visual classification method based on KPCA and parallel coordinate plot KPPCP. This method maps the raw data space into high-dimensional feature space by means of nuclear function, then the feature space is deal with PCA, finally the processed data is visualized in parallel coordinates. The experiment show that it can effectively extract the non-linear features from the raw data , enlarge the differences between the various categories, provide Interactive visualization, enhance the understanding of experts on the classification process and participation so as to get more effective classification.

Published in:

Computer Science and Service System (CSSS), 2011 International Conference on

Date of Conference:

27-29 June 2011