---
title: "Probabilistic Multi-Label Classification with Sparse Feature Learning"
authors:
  - "Yuhong Guo"
  - "Wei Xue"
doi: ""
url: "https://ijcai.org/proceedings/2013/206"
pdf: "https://ijcai.org/proceedings/2013/0206.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1373-1378"
track: ""
keywords:
---

# Probabilistic Multi-Label Classification with Sparse Feature Learning

Yuhong Guo, Wei Xue

*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.