WITHDRAWN One-step mean-squared consistency of the EM algorithm for high-dimensional Gaussian mixture models

Music City Center 

The EM algorithm has been used extensively in classification problems involving mixture models. There has been a recent surgence in the theoretical understanding of the EM algorithm in specialized versions of Gaussian mixture models, primarily in univariate models or models with fixed dimensionality. However, in practice, the use of EM extends to ultra high-dimensional datasets with surprisingly good performance. This talk will present recent results on the theoretical properties of the EM algorithm for high-dimensional Gaussian mixture models with minimal assumptions that showcases empirically optimal control of the mean squared error in just one iteration of the algorithm. The theory also provides a novel analysis method for iterative algorithms that could be of independent interest for the analysis of other algorithms in high-dimensional regimes.

Expectation-Maximization

Gaussian Mixture Models

High-dimensional

iterative algorithms

consistent estimation 

IMS