Maximum Margin Partial Label Learning

Partial label learning deals with the problem that each training example is associated with a set of \emphcandidate labels, and only one among the set is the ground-truth label. The basic strategy to learn from partial label examples is disambiguation, i.e. by trying to recover the ground-truth labeling information from the candidate label set. As one of the major machine learning techniques, maximum margin criterion has been employed to solve the partial label learning problem. Therein, disambiguation is performed by optimizing the margin between the maximum modeling output from candidate labels and that from non-candidate labels. However, in this formulation the margin between the ground-truth label and other candidate labels is not differentiated. In this paper, a new maximum margin formulation for partial label learning is proposed which aims to directly maximize the margin between the ground-truth label and all other labels. Specifically, an alternating optimization procedure is utilized to coordinate \emphground-truth label identification and \emphmargin maximization. Extensive experiments show that the derived partial label learning approach achieves competitive performance against other state-of-the-art comparing approaches.