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
Applying discrete PCA in data analysis
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multi-relational learning with Gaussian processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Stacked Gaussian Process Learning
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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Latent Dirichlet allocation is a fully generative statistical language model that has been proven to be successful in capturing both the content and the topics of a corpus of documents. Recently, it was even shown that relations among documents such as hyper-links or citations allow one to share information between documents and in turn to improve topic generation. Although fully generative, in many situations we are actually not interested in predicting relations among documents. In this paper, we therefore present a Dirichlet-multinomial nonparametric regression topic model that includes a Gaussian process prior on joint document and topic distributions that is a function of document relations. On networks of scientific abstracts and of Wikipedia documents we show that this approach meets or exceeds the performance of several baseline topic models.