Disambiguation-free partial label learning

Abstract

Partial label learning is an important weakly supervised machine learning framework. In partial label learning, each object is described by a single instance in the input space; however, in the output space, it is associated with a set of candidate labels among which only one is valid. An intuitive strategy is to disambiguate candidate labels, but this strategy tends to be misled by false positive labels; therefore, new disambiguation-free approaches need to be considered. In this paper, several algorithms are reviewed from the perspective of disambiguation and disambiguation-free strategies. First, the problem definition on partial label learning and its relationship with other related learning frameworks are given. Second, several representative partial label learning algorithms via the disambiguation strategy are introduced. Third, two of our proposed disambiguation-free algorithms are presented. Finally, the summary of this paper is given and possible future investigations on partial label learning are briefly discussed.