SSHLDA: a semi-supervised hierarchical topic model
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Hierarchical topic integration through semi-supervised hierarchical topic modeling
Proceedings of the 21st ACM international conference on Information and knowledge management
Object categorization based on a supervised mean shift algorithm
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Multi-modal image annotation with multi-instance multi-label LDA
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A聽drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that probabilistic generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.