CANSSI Saskatchewan HSCC Webinar Series January-April 2022: Jiahua Chen

Registration & talk details

This talk has been organized by the Canadian Statistical Sciences Institute (CANSSI) Saskatchewan Health Science Collaborating Centre (HSCC). Learn more and register for this talk here.

Talk Title: Gaussian Mixture Reduction based on Composite Transportation Divergence

Abstract: In many applications, researchers wish to approximate a finite Gaussian mixture distribution with a high order by one with a lower order. Examples include density estimation, recursive tracking in hidden Markov model, and belief propagation. A direct solution to such a Gaussian Mixture Reduction problem is computationally challenging due to the non-convexity of commonly employed optimality targets.

One popular line of approach is to employ some clustering-based iterative algorithms. Neither their convergence nor destination, however, are thoroughly discussed. In this paper, we propose a new GMR method by minimizing some novel composite transportation divergence (CTD). This divergence permits an easy to implement Majorization-Minimization (MM) algorithm. We prove that the MM algorithms converge under general conditions, and many existing clustering-based algorithms are special cases of our approach. We further investigate the property of this approach with various choices of cost functions and demonstrate its effectiveness and computational costs.

Event Type
Location
Zoom (to register, see CANSSI website)
Speaker
Jiahua Chen, UBC Statistics Professor
Event date time
-