Weak multi-label learning with missing labels via instance granular discrimination

Abstract

In multi-label learning, each training instance is associated with multiple class labels. It is typical in reality that relevant labels are partially missing and only a part of labels are valid, resulting in the problem of weak multi-label learning with missing labels. It is still an evident challenge to estimate the ground-truth label matrix and to generate a prediction function, especially on the multi-label data with a large number of missing labels. In this paper, we propose a multi-label learning framework within which feature structure and label manifold are both utilized to recover the incomplete label matrix and to train the classification model simultaneously. To mitigate the imbalanced risks brought by the sparse label issue, a self-adaptive penalty factor is imposed on the deviated predictions of different labels. Moreover, instance granular discrimination is incorporated in the framework to characterize the approximate distribution structure of data. Matrix vectorization, cave-convex programming (CCCP), and block coordinate descent techniques are employed to solve the proposed optimization problem. Extensive experiments illustrate the superiority of the proposed method against some well-established methods.