Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
An Introduction to Variational Methods for Graphical Models
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
An application of text categorization methods to gene ontology annotation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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This paper addresses the sparse data problem in the linear regression model, namely the number of variables is significantly larger than the number of the data points for regression. We assume that in addition to the measured data points, the prior knowledge about the input variables may be provided in the form of pair wise similarity. We presented a full Bayesian framework to effectively exploit the similarity information of the input variables for linear regression. Empirical studies with gene expression data show that the regression errors can be reduced significantly by incorporating the similarity information derived from gene ontology.