By Topic

A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods

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

2 Author(s)
C. Alzate ; Department of Electrical Engineering ESAT-SCD-SISTA, Katholieke Universiteit Leuven, B-3001 Leuven, Belgium. email: ; J. A. K. Suykens

A new formulation to spectral clustering methods based on the weighted kernel principal component analysis is presented. This formulation fits in the Least Squares Support Vector Machines (LS-SVM) framework as a primal-dual interpretation in the context of constrained optimization problems. Starting from the LS-SVM formulation to kernel PCA, a weighted approach is derived. An advantage of this method is the possibility to apply the trained clustering model to out-of-sample (test) data points without using approximation techniques such as the Nystrom method. Links with some existing spectral clustering techniques are given, showing that these techniques are particular cases of weighted kernel PCA. Simulation results with toy and real-life data show improvements in terms of generalization to new samples.

Published in:

The 2006 IEEE International Joint Conference on Neural Network Proceedings

Date of Conference:

0-0 0