@inproceedings{8fedd07abc7b437f922bc34759eb74b3,
title = "Robust L2E parameter estimation of Gaussian mixture models: Comparison with expectation maximization",
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.",
author = "Umashanger Thayasivam and Chinthaka Kuruwita and Ramachandran, {Ravi P.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-26555-1_32",
language = "English (US)",
isbn = "9783319265544",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "281--288",
editor = "Tingwen Huang and Qingshan Liu and Lai, {Weng Kin} and Sabri Arik",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
address = "Germany",
}