Semi-supervised local feature selection for data classification

Conventional feature selection methods select the same feature subset for all classes, which means that the selected features might work better for some classes than the others. Towards this end, this paper proposes a new semi-supervised local feature selection method (S2LFS) allowing to select different feature subsets for different classes. According to this method, class-specific feature subsets are selected by learning the importance of features considering each class separately. In particular, the class labels of all available data are jointly learned under a consistent constraint over the labeled data, which enables the proposed method to select the most discriminative features. Experiments on six data sets demonstrate the effectiveness of the proposed method compared to some popular feature selection methods.