Partial label metric learning by collapsing classes

Abstract

Partial label learning (PLL) is a weakly supervised learning framework proposed recently, in which the ground-truth label of training sample is not precisely annotated but concealed in a set of candidate labels, which makes the accuracy of the existing PLL algorithms is usually lower than that of the traditional supervised learning algorithms. Since the accuracy of a learning algorithm is usually closely related to its distance metric, the metric learning technologies can be employed to improve the accuracy of the existing PLL algorithms. However, only a few PLL metric learning algorithms have been proposed up to the present. In view of this, a novel PLL metric learning algorithm is proposed by using the collapsing classes model in this paper. The basic idea is first to take each training sample and its neighbor with shared candidate labels as a similar pair, while each training sample and its neighbor without shared candidate labels as a dissimilar pair, then two probability distributions are defined based on the distance and label similarity of these pairs, respectively, finally the metric matrix is obtained via minimizing the Kullback–Leibler divergence of these two probability distributions. Experimental results on six UCI data sets and four real-world PLL data sets show that the proposed algorithm can obviously improve the accuracy of the existing PLL algorithms.