University of Cambridge > Talks.cam > Machine Learning @ CUED > Convergence analysis of the EM algorithm and joint minimization of free energy

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Convergence analysis of the EM algorithm and joint minimization of free energy

Download to your calendar using vCal

If you have a question about this talk, please contact Zoubin Ghahramani .

Although the expectation-maximization (EM) algorithm has been popularly used for its computational convenience, it has been recognized that the EM algorithm works slowly in certain situations. In this study, we analyze the convergence property of the EM algorithm in terms of the minimization of the free energy, and show that the slow convergence is due to the optimization method of the free energy. The analyses suggest a different optimization can be appropriate for situations of slow convergence. Then, we propose a new speeding-up method for optimization of the free energy. The validity of the new method is confirmed by using a simple problem.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

GN06, William Gates Building. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon quan ao tre em Duane Carey

Other talks

Peri-Operative Medicine, and Obstetrics and Gynaecology Title TBC Cambridge RNA Club - IN PERSON Title TBC Using numerical simulations for solving spectral geometry problems title abstract tba
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity