Abstract
The purpose of this paper is to discuss the use of \(L_{2}E\) 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 \(L_{2}E\) 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 \(L_{2}E\) and EM for parameter estimation of Gaussian Mixture Models. Simulation examples show that the \(L_{2}E\) 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.
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This work was supported by the National Science Foundation through Grant DUE-1122296.
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Thayasivam, U., Kuruwita, C., Ramachandran, R.P. (2015). Robust \(L_{2}E\) Parameter Estimation of Gaussian Mixture Models: Comparison with Expectation Maximization. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_32
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DOI: https://doi.org/10.1007/978-3-319-26555-1_32
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