Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Semi-supervised document classification with a mislabeling error model
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Discriminative topic modeling based on manifold learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Regularized semi-supervised latent dirichlet allocation for visual concept learning
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
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Latent topic models are used to analyze the low-dimensional semantic meaning of documents and images, which are widely applied to object categorization. However, object labeling is expensive and subjective in real applications. Thus, a hybrid semi-supervised topic model is proposed, which uses a small amount of labels to help the generative topic model find semantic topics and cluster the unlabeled data to the same class. We applied the model to obtain the semi-supervised LDA and pLSA methods. Experimental results on natural scene and head pose classification tasks show that the proposed method remains promising using only partial labels in the training process, which demonstrates the effectiveness of the proposed method.