On model-based clustering of skewed matrix and tensor data

10/30/2020 - 3:00pm
Speaker(s) / Presenter(s): 
Dr. Volodymyr Melnykov, University of Alabama

Abstract: The existing finite mixture modeling and model-based clustering literature focuses primarily on the analysis of multivariate data observed in the form of vectors, with each element representing a specific feature. In this setting, multivariate Gaussian mixture models have been the most commonly used. Due to severe modeling issues observed when normal components cannot provide adequate fit to groups, much attention is paid to developing models capable of accounting for skewness in data. We target the problem of mixture modeling with components that can handle skewness in matrix- and tensor-valued data. The proposed developments open a wide range of possible modeling capabilities, with numerous applications, as illustrated in the talk.


Type of Event (for grouping events):
Enter your linkblue username.
Enter your linkblue password.
Secure Login

This login is SSL protected