Robust Multi-label Feature Selection with Missing Labels

With the fast development of social networks, high-dimensionality is becoming an intractable problem in many machine learning and computer vision tasks. This phenomenon also exists in the field of multi-label classification. So far many supervised or semi-supervised multi-label feature selection methods have been proposed to reduce the feature dimension of training samples. However, almost all existing feature selection works focus on multi-label learning with complete labels. In fact, labels are very expensive to obtain and the training instances usually have an incomplete/partial set of labels (some labels are randomly missed). Very few researchers pay attention to the problem of multi-label feature selection with missing labels. In this paper, we propose a robust model to solve the above problem. We recover the missing labels by a linear regression model and select the most discriminative feature subsets simultaneously. The effective l(2),(p)-norm (0 < p <= 1) regularization is imposed on the feature selection matrix. The iterative reweighted least squares (IRLS) algorithm is used to solve the optimization problem. To verify the effectiveness of the proposed method, we conduct experiments on five benchmark datasets. Experimental results show that our method has superior performance over the state-of-the-art algorithms.