Trace Ratio Criterion for Feature Selection  Feiping Nie,
Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui Zhang,
Shuicheng Yan  Fisher score and Laplacian score are two popular
feature selection algorithms, both of which belong to the
general graph-based feature selection framework. In this
framework, a feature subset is selected based on the
corresponding score (subset-level score), which is calculated in
a trace ratio form. Since the number of all possible feature
subsets is very huge, it is often prohibitively expensive in
computational cost to search in a brute force manner for the
feature subset with the maximum subset-level score. Instead of
calculating the scores of all the feature subsets, traditional
methods calculate the score for each feature, and then select
the leading features based on the rank of these feature-level
scores. However, selecting the feature subset based on the
feature-level score cannot guarantee the optimum of the subset-
level score. In this paper, we directly optimize the su