Consistent feature selection and its application to face recognition

In this paper we consider feature selection for face recognition using both labeled and unlabeled data. We introduce the weighted feature space in which the global separability between different classes is maximized and the local similarity of the neighboring data points is preserved. By integrating the global and local structures, a general optimization framework is formulated. We propose a simple solution to this problem, avoiding the matrix eigen-decomposition procedure which is often computationally expensive. Experimental results demonstrate the efficacy of our approach and confirm that utilizing labeled and unlabeled data together does help feature selection with small number of labeled samples.