A Fast Globally Supervised Learning Algorithm for Gaussian Mixture Models

Authors:
Jiyong Ma;Wen Gao
Affiliations:
-;-
Venue:
WAIM '00 Proceedings of the First International Conference on Web-Age Information Management
Year:
2000

Citing 3
Cited 1

Elements of information theory

Elements of information theory
Statistical physics, mixtures of distributions, and the EM algorithm

Neural Computation
Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques

IEEE Transactions on Neural Networks

Supervised learning of Gaussian mixture models for visual vocabulary generation

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a fast globally supervised learning algorithm for Gaussian Mixture Models based on the maximum relative entropy (MRE) is proposed. To reduce the computation complexity in Gaussian component probability densities, the concept of quasi-Gaussian probability density is used to compute the simplified probabilities. For four different learning algorithms such as the maximum mutual information algorithm (MMI), the maximum likelihood estimation (MLE), the generalized probabilistic descent (GPD) and the maximum relative entropy (MRE) algorithm, the random experiment approach is used to evaluate their performances. The experimental results show that the MRE is a better alternative algorithm in accuracy and training speed compared with GPD, MMI and MLE.