Statistics seminar Inria, Sept 17, 2pm, by H. Nguyen, La Trobe University, Melbourne

[Florence Forbes]

Dear all,

Dr. Hien Nguyen from La Trobe University  will give a talk on his ongoing research on

Minibatch and incremental learning of exponential family mixtures, and the soft k-means clustering problem

Seminar  by Hien Nguyen, La Trobe University, Melbourne, Australia

Monday September 17, 2018, 14:00 – 15:00, room F107, INRIA Montbonnot Saint-Martin

Abstract.

Mixtures of exponential family distributions are an important class of probabilistic models that form the basis of many model-based clustering approaches. The EM algorithm is typically used to learn the parameter of such models, from data. When data are large in size and dimensionality, the computational performance of the EM algorithm can be impeded by memory issues and computational bottlenecks. 

Recently, there has been a trend towards the use of stochastic-approximation algorithms, in order to circumvent the bottlenecks of traditional algorithms. In this talk, we present a stochastic EM algorithm framework that can be used for minibatch and incremental learning of exponential family mixtures. The algorithm is provably convergent, and covers the important special case of Gaussian mixture models. We also demonstrate a modification of the algorithm that can be used to incrementally solve the soft k-means problem.