Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Exploring the similarity space
ACM SIGIR Forum
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Training products of experts by minimizing contrastive divergence
Neural Computation
A New Learning Algorithm for Mean Field Boltzmann Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Classification of Web Documents Using a Graph Model
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
The Journal of Machine Learning Research
Bayesian learning in undirected graphical models: approximate MCMC algorithms
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The rate adapting poisson model for information retrieval and object recognition
ICML '06 Proceedings of the 23rd international conference on Machine learning
Harmonium Models for Video Classification
Statistical Analysis and Data Mining
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A coarse-to-fine framework to efficiently thwart plagiarism
Pattern Recognition
Locally discriminative topic modeling
Pattern Recognition
A multi-level matching method with hybrid similarity for document retrieval
Expert Systems with Applications: An International Journal
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A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.