Work

K-MLE: A fast algorithm for learning statistical mixture models

Year: 2012

Type: conference-paper

Abstract: We present a fast and generic algorithm, k-MLE, for learning statistical mixture models using maximum likelihood estimators. We prove theoretically that k-MLE is dually equivalent to a Bregman k-means for the case of mixtures of exponential families (e.g., Gaussian mixture models). k-MLE is used to initialize appropriately the expectation-maximization algorithm. We also show experimentally that k-MLE outperforms the EM technique with standard initialization by considering modeling color images using high-dimensional Gaussian mixture models.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2012.6288022

Author: Frank Nielsen

Institution: Sony Computer Science Laboratories

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2153238387

FWCI: 3.434

Cites: 63

Cited by: 34

Related to: 10