Multilabel classification with label correlations and missing labels

Abstract

Many real-world applications involve multilabel classification, in which the labels can have strong interdependencies and some of them may even be missing. Existing multilabel algorithms are unable to handle both issues simultaneously. In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations. By integrating out the missing information, it also provides a disciplined approach to the handling of missing labels. The inference procedure is simple, and the optimization subproblems are convex. Experiments on a number of real-world data sets with both complete and missing labels demonstrate that the proposed algorithm can consistently outperform stateof-the-art multilabel classification algorithms.