Multi-label learning with missing features and labels and its application to text categorization

Abstract

• This paper proposes a new method MMFL to address the problem of missing features and missing labels simultaneously in multi-label learning. • Matrix decomposition technology is used to recover the missing values of features and labels. In addition, to overcome the problem of tail labels in matrix factorization, an extra classifier for the sparse tail labels is built. • The experiment on nine multi-label text categorization data sets show that our proposed method achieves a competitive performance in multi-label learning with missing features and labels. In multi-label learning, researchers usually assume that the training data set is complete. However, this assumption is not always hold in real applications, e.g., the features or labels are missing for some data examples. Existing algorithms mainly focus on the problem of missing labels, and ignore the problem of missing features. This paper proposes a novel multi-label learning algorithm named MMFL, i.e., Multi-label learning with Missing Features and Labels, which can deal with the problem of missing features and labels simultaneously. First, we try to recover the missing values of features and labels by matrix factorization, and then learn a classification model from the latent feature space to the latent label space. Second, to overcome the problem of tail labels in matrix factorization, we build an extra classifier for the sparse tail labels. Besides, the manifold regularization technology is used to keep the manifold structures of instance similarity and label correlation. The effectiveness of our proposed method is verified by comparing it with the state-of-the-art approaches over eight multi-label benchmark data sets.