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

on the September 17, 2018

September 17 from 2 pm to 3 pm
Hien Nguyen from La Trobe University (Melbourne, Australia) will talk about "Minibatch and incremental learning of exponential family mixtures, and the soft k-means clustering problem". The event will take place on September 17 at 2 pm at INRIA Montbonnot Saint-Martin, room F107.
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.
Published on September 18, 2018

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room F107
INRIA Montbonnot Saint-Martin