Solving the partial label learning problem: an instance-based approach
Min-Ling Zhang, Fei Richard Yu · 2015
In partial label learning, each training example is associated with a set of candidate labels, among which only one is valid. An intuitive strategy to learn from partial label examples is to treat all can-didate labels equally and make prediction by av-eraging their modeling outputs. Nonetheless, this strategy may suffer from the problem that the mod-eling output from the valid label is overwhelmed by those from the false positive labels. In this pa-per, an instance-based approach named IPAL is pro-posed by directly disambiguating the candidate la-bel set. Briefly, IPAL tries to identify the valid label of each partial label example via an iterative label propagation procedure, and then classifies the un-seen instance based on minimum error reconstruc-tion from its nearest neighbors. Extensive experi-ments show that IPAL compares favorably against the existing instance-based as well as other state-of-the-art partial label learning approaches. 1