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张驰浩 博士:Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
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Title:
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
Speaker:
张驰浩 博士,日本东京大学
Inviter: 张世华 研究员
Time & Venue:

2021.10.26 18:00 N625

Abstract:

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 considering the heterogeneity of noise is superior or competitive to the state-of-the-art methods.

Affiliation:  

学术报告中国科学院数学与系统科学研究院应用数学研究所
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