Semi-Supervised Top-k Feature Selection with a General Optimization Framework

Feature selection is widely used in multimedia applications to determine informative features from high-dimensional data. Due to the explosive growth of the data size and the expensive cost of obtaining labeled data, it is increasingly demanded to utilize both labeled and unlabeled data for feature selection. In this paper, we introduce the l2,0-norm in semi-supervised feature selection, which is able to select exact k informative features. Due to the non-convexity of l2,0-norm, we further devise an efficient coordinate-descent-based algorithm to solve the l2,0-norm constraint, which facilitates the application of l2,0-norm to more complex applications, including but not limited to the proposed model in this study. We experimentally verify the effectiveness of the proposed l2,0-norm-based semi-supervised method and the efficiency of the proposed optimization algorithm.