Multi-label classification is a critical problem in many areas of data analysis such as image labeling and text categorization. In this paper we propose a probabilistic multi-label classification model based on novel sparse feature learning. By employing an individual sparsity inducing ℓ1-norm and a group sparsity inducing ℓ2;1-norm, the proposed model has the capacity of capturing both label interdependencies and common predictive model structures. We formulate this sparse norm regularized learning problem as a non-smooth convex optimization problem, and develop a fast proximal gradient algorithm to solve it for an optimal solution. Our empirical study demonstrates the efficacy of the proposed method on a set of multi-label tasks given a limited number of labeled training instances.

23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
School of Computer Science

Guo, Y, & Xue, W. (Wei). (2013). Probabilistic multi-label classification with sparse feature learning. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1373–1379).