CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels

Publisher:
the MIT Press
Publication Type:
Conference Proceeding
Citation:
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012, pp. 109 - 117
Issue Date:
2012-01
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In this paper, we present a simple but effective method for multi-label classification (MLC), termed Correlated Logistic Models (Corrlog), which extends multiple Independent Logistic Regressions (ILRs) by modeling the pairwise correlation between labels. Algorithmically, we propose an efficient method for learning parameters of Corrlog, which is based on regularized maximum pseudolikelihood estimation and has a linear computational complexity with respect to the number of labels. Theoretically, we show that Corrlog enjoys a satisfying generalization bound which is independent of the number of labels. The effectiveness of Corrlog on modeling label correlations is illustrated by a toy example, and further experiments on real data show that Corrlog achieves competitive performance compared with popular MLC algorithms.
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