  Partial Label Learning (PLL) aims to learn from the data where each training
example is associated with a set of candidate labels, among which only one is
correct. The key to deal with such problem is to disambiguate the candidate
label sets and obtain the correct assignments between instances and their
candidate labels. In this paper, we interpret such assignments as
instance-to-label matchings, and reformulate the task of PLL as a matching
selection problem. To model such problem, we propose a novel Graph Matching
based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM)
scheme is incorporated owing to its excellent capability of exploiting the
instance and label relationship. Meanwhile, since conventional one-to-one GM
algorithm does not satisfy the constraint of PLL problem that multiple
instances may correspond to the same label, we extend a traditional one-to-one
probabilistic matching algorithm to the many-to-one constraint, and make the
proposed framework accommodate to the PLL problem. Moreover, we also propose a
relaxed matching prediction model, which can improve the prediction accuracy
via GM strategy. Extensive experiments on both artificial and real-world data
sets demonstrate that the proposed method can achieve superior or comparable
performance against the state-of-the-art methods.
