Robust L2E parameter estimation of Gaussian mixture models: Comparison with expectation maximization

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

1 Scopus citations

Abstract

The purpose of this paper is to discuss the use of L2E estimation that minimizes integrated square distance as a practical robust estimation tool for unsupervised clustering. Comparisons to the expectation maximization (EM) algorithm are made. The L2E approach for mixture models is particularly useful in the study of big data sets and especially those with a consistent numbers of outliers. The focus is on the comparison of L2 E and EM for parameter estimation of Gaussian Mixture Models. Simulation examples show that the L2E approach is more robust than EM when there is noise in the data (particularly outliers) and for the case when the underlying probability density function of the data does not match a mixture of Gaussians.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
EditorsTingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
PublisherSpringer Verlag
Pages281-288
Number of pages8
ISBN (Print)9783319265544
DOIs
StatePublished - Jan 1 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: Nov 9 2015Nov 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9491
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Neural Information Processing, ICONIP 2015
CountryTurkey
CityIstanbul
Period11/9/1511/12/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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