【15-15期VALSE Webinar活动】 报告嘉宾2：马占宇（北京邮电大学）
主持人：王琦（西北工业大学）
报告题目：Extended Variational Inference for Non-Gaussian Statistical Models http://valser.org/webinar/slide/slides/20150520/Zhanyu_Ma_Valse_Webinar20150520.pdf 报告时间：2015年5月20日晚21:00（北京时间）
文章信息：
[1] Z. Ma, A.E. Teschendorff, A. Leijon, and J. Guo, “Variational Bayesian Matrix Factorization for Bounded Support Data”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Volume 37, Issue 4, pp. 876 – 889, Apr. 2015.
[2] Z. Ma, A. Leijon, “Bayesian Estimation of Beta Mixture Models with Variational Inference”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 33, pp. 2160 – 2173, Nov. 2011.
[3] Z. Ma, P. K. Rana, J. Taghia, M. Flierl, and A. Leijon, “Bayesian Estimation of Dirichlet Mixture Model with Variational Inference”, Pattern Recognition (PR), Volume 47, Issue 9, pp. 3143-3157, September 2014.
[4] J. Taghia, Z. Ma, A. Leijon, “Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Volume: 36, Issue9, pp. 1701-1715, September, 2014.
报告简介：Recent research demonstrate that the usage of non-Gaussian statistical models is advantageous in applications where the data are not Gaussian distributed. With conventionally applied model estimation methods, e.g., maximum likelihood estimation and Bayesian estimation, we cannot derive analytically tractable solution for non-Gaussian statistical models. In order to obtain closed-form solution, we extend the commonly used variational inference (VI) framework via lower-bound approximation, by utilizing convexity/relative convexity of the integrants in the non-Gaussian distributions. In this presentation, we introduce the principles of the extended variational inference (EVI) and demonstrate its advantages in non-Gaussian mixture models and bounded support matrix factorization. We also show the advantages of non-Gaussian statistical models in real life applications, such as speech coding, 3D depth map enhancement, and DNA methylation analysis. Here, we restrict our attention to the non-Gaussian distribution in the exponential family
报告人简介：Zhanyu Ma received the M. Eng degree in signal and information processing from Beijing University of Posts and Telecommunications (BUPT), China, and the Ph. D. degree in electrical engineering from Royal Institute of Technology (KTH), Sweden, in 2007 and 2011, respectively. He has been an associate professor at the Beijing University of Posts and Telecommunications, Beijing, China, since 2014. From 2012 to 2013, he has been a Postdoctoral research fellow in the School of Electrical Engineering, KTH, Sweden. His research interests include pattern recognition and machine learning fundamentals with a focus on applications in multimedia signal processing, data mining, biomedical signal processing, and bioinformatics. |
