Boosting Kernel Discriminant Analysis for pattern classification

2007 ◽  
Author(s):  
Shinji Kita ◽  
Seiichi Ozawa ◽  
Satoshi Maekawa ◽  
Shigeo Abe
Keyword(s):  
Discriminant Analysis ◽  
Pattern Classification ◽  
Kernel Discriminant Analysis

ADAPTIVE KERNEL DISCRIMINANT ANALYSIS AND ITS APPLICATIONS ON PATTERN RECOGNITION

2006 ◽  
Vol 03 (04) ◽  
pp. 329-337
Author(s):  
JUN-BAO LI ◽  
JENG-SHYANG PAN
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Pattern Classification ◽  
Input Data ◽  
Feature Space ◽  
Extraction Techniques ◽  
Kernel Discriminant Analysis ◽  
Adaptive Kernel ◽  
Discriminatory Information

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. In this paper, we present an extension of KFD method based on the data-dependent kernel, called the adaptive kernel discriminant analysis (AKDA), for feature extraction and pattern classification. AKDA is more adaptive to the input data than KDA owing to the optimization of projection from input space to feature space with the data-dependent kernel, which enhances the performance of KDA. Experimental results on ORL, Yale and MNIST database show that the proposed AKDA gives the higher performance than KDA.

Sign in / Sign up

Export Citation Format

Share Document