EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Saliency, Scale and Image Description
International Journal of Computer Vision
Training products of experts by minimizing contrastive divergence
Neural Computation
Variational Extensions to EM and Multinomial PCA
ECML '02 Proceedings of the 13th European Conference on Machine Learning
A New Learning Algorithm for Mean Field Boltzmann Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion 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
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
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data
Proceedings of the 24th international conference on Machine learning
Generalized component analysis for text with heterogeneous attributes
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
Semi-supervised learning of compact document representations with deep networks
Proceedings of the 25th international conference on Machine learning
On the quantitative analysis of deep belief networks
Proceedings of the 25th international conference on Machine learning
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th international conference on Machine learning
International Journal of Approximate Reasoning
A new dual wing harmonium model for document retrieval
Pattern Recognition
PLSI: The True Fisher Kernel and beyond
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Proceedings of the 18th ACM conference on Information and knowledge management
A novel dual wing harmonium model aided by 2-D wavelet transform subbands for document data mining
Expert Systems with Applications: An International Journal
Learning to rank with (a lot of) word features
Information Retrieval
A coarse-to-fine framework to efficiently thwart plagiarism
Pattern Recognition
Two Distributed-State Models For Generating High-Dimensional Time Series
The Journal of Machine Learning Research
Larger residuals, less work: active document scheduling for latent dirichlet allocation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
A multi-level matching method with hybrid similarity for document retrieval
Expert Systems with Applications: An International Journal
Learning algorithms for the classification restricted Boltzmann machine
The Journal of Machine Learning Research
Training restricted Boltzmann machines: An introduction
Pattern Recognition
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Probabilistic modelling of text data in the bag-of-words representation has been dominated by directed graphical models such as pLSI, LDA, NMF, and discrete PCA. Recently, state of the art performance on visual object recognition has also been reported using variants of these models. We introduce an alternative undirected graphical model suitable for modelling count data. This "Rate Adapting Poisson" (RAP) model is shown to generate superior dimensionally reduced representations for subsequent retrieval or classification. Models are trained using contrastive divergence while inference of latent topical representations is efficiently achieved through a simple matrix multiplication.