Partial label learning with emerging new labels

Abstract

Partial label learning deals with the problem where each training instance is associated with a set of candidate labels, among which only one is valid. Existing approaches on partial label learning assume that the scale of label space is fixed, however, this assumption may not be satisfied in open and dynamic environment. In this paper, the first attempt towards the problem of partial label learning with emerging new labels is presented. There are mainly three challenges in this task, namely new label detection, effective classification, and efficient model updating. Specifically, a new method is proposed to address these challenges which consists of three parts: (1) An ensemble-based detector that identifies instances from new labels while also assigns candidate labels to instances which may belong to known labels. (2) An effective classification mechanism that involves data pool construction and label disambiguation process. (3) An efficient updating procedure that adapts both the detector and classifier to new labels without training from scratch. Our experiments on artificial and real-world partial label data sets validate the effectiveness of the proposed method in dealing with emerging labels for partial label learning.