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Principal component analysis (PCA) has been widely applied in the area of computer science. It is well-known that PCA is a popular transform method and the transform result is not directly related to a sole feature component of the original sample. However, in this paper, we try to apply principal components analysis (PCA) to feature selection. The proposed method well addresses the feature selection...
In this paper we propose a novel feature selection method based on linear discriminant analysis (LDA). To view feature selection as a numerical computation problem, the paper shows, for the first time, that it is feasible to employ LDA for feature selection. The proposed method also shows that different components statistically have different effects on the feature selection result, which can be evaluated...
Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been...
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