Modified kernel-based nonlinear feature extraction

被引：0
|
作者：
Dai, G [1 ]
Qian, YT [1 ]
Jia, S [1 ]
机构：
[1] Zhejiang Univ, Coll Comp Sci, Zhejiang, Peoples R China
来源：
2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS: DESIGN AND IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS INDUSTRY TECHNOLOGY TRACKS MACHINE LEARNING FOR SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING SIGNAL PROCESSING FOR EDUCATION | 2004年
关键词：
deature extraction; kernel Fisher discriminant analysis; face recognition;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Feature extraction techniques are widely used in many applications to pro-process data in order to reduce the complexity of subsequent processes. A group of kernel-based Fisher discrminant analysis (KFDA) algorithms has attracted much attention due to their high performance. In this paper, the inherent limitations of those KFDA algorithms have been discussed and the novel algorithm will be proposed to effectively overcome those limitations. Experimental results on face recognition suggest that this proposed algorithm is superior to the existing methods in terms of correct classification rate.
引用
收藏
页码：721 / 724
页数：4
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