CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels

Wei Bian, Bo Xie, Dacheng Tao
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:109-117, 2012.

Abstract

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 pseudo-likelihood 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-bian12, title = {CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels}, author = {Bian, Wei and Xie, Bo and Tao, Dacheng}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {109--117}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/bian12/bian12.pdf}, url = {https://proceedings.mlr.press/v22/bian12.html}, abstract = {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 pseudo-likelihood 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.} }
Endnote
%0 Conference Paper %T CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels %A Wei Bian %A Bo Xie %A Dacheng Tao %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-bian12 %I PMLR %P 109--117 %U https://proceedings.mlr.press/v22/bian12.html %V 22 %X 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 pseudo-likelihood 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.
RIS
TY - CPAPER TI - CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels AU - Wei Bian AU - Bo Xie AU - Dacheng Tao BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-bian12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 109 EP - 117 L1 - http://proceedings.mlr.press/v22/bian12/bian12.pdf UR - https://proceedings.mlr.press/v22/bian12.html AB - 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 pseudo-likelihood 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. ER -
APA
Bian, W., Xie, B. & Tao, D.. (2012). CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:109-117 Available from https://proceedings.mlr.press/v22/bian12.html.

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