Batch and on-line parameter estimation of Gaussian mixtures based on the joint entropy

Yoram Singer, Manfred K. Warmuth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line learning. In contrast to gradient descent and EM, which estimate the mixture's covariance matrices, the proposed method estimates the inverses of the covariance matrices. Furthermore, the new parameter estimation procedure can be applied in both on-line and batch settings. We show experimentally that it is typically faster than EM, and usually requires about half as many iterations as EM.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998
PublisherNeural information processing systems foundation
Pages578-584
Number of pages7
ISBN (Print)0262112450, 9780262112451
StatePublished - Jan 1 1999
Externally publishedYes
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: Nov 30 1998Dec 5 1998

Other

Other12th Annual Conference on Neural Information Processing Systems, NIPS 1998
CountryUnited States
CityDenver, CO
Period11/30/9812/5/98

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Singer, Y., & Warmuth, M. K. (1999). Batch and on-line parameter estimation of Gaussian mixtures based on the joint entropy. In Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998 (pp. 578-584). Neural information processing systems foundation.