相关下载详见 “视觉计算研究论坛”「SIGVC BBS」：http://www.sigvc.org/bbs/thread-36-1-2.html

Premise:  you have got a set of    
                features(measurement) of  samples.
•identify those variables that do not contribute to the classification task.

•find a transformation from the p measurements to a lower-dimensional feature space.

 

•select those d variables that contribute most to discrimination.

•Feature selection criteria: error rate, probabilistic distance, recursive calculation of separability measurement, criteria based on scatter matrices.

 

• PCA方法现阶段的应用？现阶段的计算能力上再讨论PCA还有没有意义？

•降维还有没有必要？

 

 

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