By Topic

Feature Extraction Based on Mixture Probabilistic Kernel Principal Component Analysis

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

3 Author(s)
Zhao Huibo ; Coll. of Autom., Northwestern Polytech. Univ., Xi''an, China ; Pan Quan ; Cheng Yongmei

Feature extraction of training samples and testing samples face the problem of the high non-linear by complexity of the distribution of the samples. In contrast to linear PCA, KPCA is capable of capturing part of the higher-order statistics which are particularly important for encoding image structure. The probabilistic kernel principal component analysis (PKPCA), defines PPCA probability model by non-linear mapping in the high-dimensional feature space. This paper presents the mix model of the probability of kernel principal component analysis (MPKPCA) method, which adopt a non-linear mapping to make the data from low-dimensional space to the high-dimensional kernel space, in kernel space, using the mixed probability principal component analysis (MPPCA), it combines the advantages of kernel principal component analysis (KPCA) and MPPCA characteristics. Experimental results under complex scenery demonstrate that the proposed algorithm is feasibility and effectiveness.

Published in:

Information Technology and Applications, 2009. IFITA '09. International Forum on  (Volume:3 )

Date of Conference:

15-17 May 2009