Proceedings

Probabilistic Multi-Label Classification with Sparse Feature Learning

Yuhong Guo, Wei Xue

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1373-1378.

Abstract

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.

Yuhong Guo, and Wei Xue. 2013. Probabilistic Multi-Label Classification with Sparse Feature Learning. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 1373–1378. https://ijcai.org/proceedings/2013/206
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