Robust kernel discriminant analysis and its application to feature extraction and recognition

2006 ◽  
Vol 69 (7-9) ◽  
pp. 928-933 ◽  
Author(s):  
Zhizheng Liang ◽  
David Zhang ◽  
Pengfei Shi
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
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.

Gear Fault Classification Using Kernel Discriminant Analysis

2006 ◽  
Vol 321-323 ◽  
pp. 1556-1559
Author(s):  
Wei Hua Li ◽  
Kang Ding ◽  
Tie Lin Shi ◽  
Guang Lan Liao
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Operating Conditions ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Kernel Discriminant Analysis ◽  
Vibration Signals ◽  
Gearbox Vibration ◽  
Gear Fault

This paper presents a study of KDA(kernel discriminant analysis) in gearbox failure feature extraction and classification. Experimental gearbox vibration signals measured from normal, gear small spall, gear severe spall and gear wear operating conditions are analyzed using either KPCA(kernel principal component analysis) or KDA as the feature extraction and fault classification methods. Experiment results indicate the effectiveness and thesuperiority of KDA for gear fault classification over KPCA.

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