True-negative label selection for large-scale multi-label learning

Atsushi Kanehira, Andrew Shin, Tatsuya Harada

Abstract: In this paper, we focus on training a classifier from large-scale data with incompletely assigned labels. In other words, we treat samples with following properties: 1. assigned labels are definitely positive, 2. absent labels are not necessarily negative, and 3. samples are allowed to take more than one label. These properties are frequently found in various kinds of computer vision tasks, including image and video classification and retrieval. Many online algorithms for multi-label task employ label sampling, which selects a label pair that reduces the largest penalty to update the model, thereby avoiding waste of computation. In the setting above, however, there are “false-negative” labels, which are originally positive labels but regarded as negative. Since it is high likely for label sampling to select these labels as negative labels in the sampled pair, it may severely degrade classification performance. In order to solve this problem while preserving convergence property of the online algorithms, we propose a novel label sampling approach, which aims to fetch “true-negative” labels via false-negativeness measure based on independently trained uni-class classifiers. Experimental results show the effectiveness of our approach.