Accession Number:

ADA295637

Title:

On Convergence Properties of the EM Algorithm for Gaussian Mixtures.

Descriptive Note:

Memorandum rept.,

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s):

• Jordan, Michael
• Xu, Lei

Report Date:

1995-01-17

Pagination or Media Count:

11.0

Abstract:

Expectation-MaximizationEM algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. AN

Descriptors:

• *ALGORITHMS
• *NEURAL NETS
• *LEARNING
• MATHEMATICAL MODELS
• OPTIMIZATION
• DATA MANAGEMENT
• MAXIMUM LIKELIHOOD ESTIMATION
• MATRICES(MATHEMATICS)
• PROBABILITY
• CONVERGENCE
• ITERATIONS
• MARKOV PROCESSES

Subject Categories:

• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE