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Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise.

08:00 EDT 10th October 2019 | BioPortfolio

Summary of "Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise."

Matrix decomposition is a popular and fundamental approach in machine learning and data mining. It has been successfully applied into various fields. Most matrix decomposition methods focus on decomposing a data matrix from one single source. However, it is common that data are from different sources with heterogeneous noise. A few of matrix decomposition methods have been extended for such multi-view data integration and pattern discovery. While only few methods were designed to consider the heterogeneity of noise in such multi-view data for data integration explicitly. To this end, we propose a joint matrix decomposition framework (BJMD), which models the heterogeneity of noise by Gaussian distribution in a Bayesian framework. We develop two algorithms to solve this model: one is a variational Bayesian inference algorithm, which makes full use of the posterior distribution; and another is a maximum a posterior algorithm, which is more scalable and can be easily paralleled. Extensive experiments on synthetic and real-world datasets demonstrate that BJMD is superior or competitive to the state-of-the-art methods.

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This article was published in the following journal.

Name: IEEE transactions on pattern analysis and machine intelligence
ISSN: 1939-3539
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