Efficient algorithms for learning mixture models

We study the statistical learning problems for a class of probabilistic models called mixture models. Mixture models are usually used to model settings where the observed data consists of different sub-populations, yet we only have access to a limited number of samples of the pooled data. It includes many widely used models such as Gaussian mixtures models, Hidden Markov Models, and topic models. We focus on parametric learning: given unlabeled data generated according to a mixture model, infer about the parameters of the underlying model. The hierarchical structure of the probabilistic model leads to non-convexity of the likelihood function in the model parameters, thus imposing great challenges in finding statistically efficient and computationally efficient solutions. We start with a simple, yet general setup of mixture model in the first part. We study the problem of estimating a low rank M x M matrix which represents a discrete distribution over M2 outcomes, given access to sample drawn according to the distribution. We propose a learning algorithm that accurately recovers the underlying matrix using 9(M) number of samples, which immediately lead to improved learning algorithms for various mixture models including topic models and HMMs. We show that the linear sample complexity is actually optimal in the min-max sense. There are "hard" mixture models for which there exist worst case lower bounds of sample complexity that scale exponentially in the model dimensions. In the second part, we study Gaussian mixture models and HMMs. We propose new learning algorithms with polynomial runtime. We leverage techniques in probabilistic analysis to prove that worst case instances are actually rare, and our algorithm can efficiently handle all the non-worst case instances. In the third part, we study the problem of super-resolution. Despite the lower bound for any deterministic algorithm, we propose a new randomized algorithm which complexity scales only quadratically in all dimensions, and show that it can handle any instance with high probability over the randomization.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Huang, Qingqing, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Munther A. Dahleh.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/107337

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/107337