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Activity Number: 697
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319505 View Presentation
Title: Sparse Clustering of High-Dimensional Gaussian Mixtures
Author(s): Jing Ma* and Tony Cai and Linjun Zhang
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: Normal mixtures ; Unsupervised clustering ; EM algorithm ; High-dimensional inference

This work studies the problem of high dimensional clustering when the observations come from a two-class normal mixture with equal covariances. In particular, we investigate the performance of the EM algorithm in estimating sparse discriminant vectors using regularized maximum likelihood. With an appropriate initialization, our results show that the EM algorithm attains an estimator with faster convergence rate than the commonly used method of moments. We further establish that unsupervised clustering equipped with the above estimator yields superior performance in terms of misclustering rate.

Authors who are presenting talks have a * after their name.

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