Maximum Margin Partial Label Learning
Fei Richard Yu, Min-Ling Zhang · 2015
Partial label learning deals with the problem that each training example is associated with a set of candidate 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 la-beling 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. There-in, 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 max-imize the margin between the ground-truth label and all other labels. Specifically, an alternating optimization procedure is utilized to coordinate ground-truth label identification and margin max-imization. Extensive experiments show that the derived partial label learning approach achieves competitive performance against other state-of-the-art comparing approaches. 1.