Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Matrix algorithms
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Stable local dimensionality reduction approaches
Pattern Recognition
Probabilistic dyadic data analysis with local and global consistency
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A new dual wing harmonium model for document retrieval
Pattern Recognition
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A general learning framework using local and global regularization
Pattern Recognition
Unsupervised topic detection model and its application in text categorization
Proceedings of the CUBE International Information Technology Conference
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Topic modeling is a powerful tool for discovering the underlying or hidden structure in text corpora. Typical algorithms for topic modeling include probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA). Despite their different inspirations, both approaches are instances of generative model, whereas the discriminative structure of the documents is ignored. In this paper, we propose locally discriminative topic model (LDTM), a novel topic modeling approach which considers both generative and discriminative structures of the data space. Different from PLSA and LDA in which the topic distribution of a document is dependent on all the other documents, LDTM takes a local perspective that the topic distribution of each document is strongly dependent on its neighbors. By modeling the local relationships of documents within each neighborhood via a local linear model, we learn topic distributions that vary smoothly along the geodesics of the data manifold, and can better capture the discriminative structure in the data. The experimental results on text clustering and web page categorization demonstrate the effectiveness of our proposed approach.