Probabilistic Multi-Label Classification with Sparse Feature Learning

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 `1-norm and a group sparsity inducing `2, 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.

Authors

• Yuhong Guo Carleton University Canada CIFAR AI Chair, Amii
• Wei Xue Hong Kong University of Science and Technology

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