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
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
MedLDA: maximum margin supervised topic models for regression and classification
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
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised topic modeling for image annotation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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
Object categorization with sketch representation and generalized samples
Pattern Recognition
Hi-index | 0.01 |
Latent topic models are applied to analyze the low-dimensional semantic meaning of documents and images, which are widely used in object categorization. However, the unsupervised topic model cannot guarantee that the learned topics have a good relation with class labels, while manually aligning and labeling all training images are expensive and subjective in real applications. Aiming at using a small amount of partial labels to find topics much more suitable for classification, joint distribution from multi-conditional learning is adopted in this paper to generate semi-supervised topic models. Semi-supervised LDA and pLSA models are proposed when the joint distribution is known or partially known. Experimental results on natural scene categorization 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.