Probabilistic multi-label classification with sparse feature learning
Yuhong Guo, Wei Xue · 2013
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 interdepen-dencies and common predictive model structures. We formulate this sparse norm regularized learn-ing problem as a non-smooth convex optimization problem, and develop a fast proximal gradient algo-rithm to solve it for an optimal solution. Our empir-ical study demonstrates the efficacy of the proposed method on a set of multi-label tasks given a limited number of labeled training instances. 1