Volume 3, Issue 4, August 2015, Page: 171-176
Multivariate Outlier Detection Using Independent Component Analysis
Md. Shamim Reza, Department of Mathematics, Pabna University of Science & Technology, Pabna, Bangladesh
Sabba Ruhi, Department of Mathematics, Pabna University of Science & Technology, Pabna, Bangladesh
Received: May 17, 2015;       Accepted: May 29, 2015;       Published: Jun. 19, 2015
DOI: 10.11648/j.sjams.20150304.11      View  4582      Downloads  182
Abstract
The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection, data clustering and multivariate data visualization etc. Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be misleading at best. In addition, outliers themselves are often the special points of interest in many practical situations and their identification is the main purpose of the investigation. This paper takes an attempt a new and novel method formultivariate outlier detection using ICA and compares with different outlier detection techniques in the literature.
Keywords
Kurtosis, Outlier, Independent Component Analysis, Normality
To cite this article
Md. Shamim Reza, Sabba Ruhi, Multivariate Outlier Detection Using Independent Component Analysis, Science Journal of Applied Mathematics and Statistics. Vol. 3, No. 4, 2015, pp. 171-176. doi: 10.11648/j.sjams.20150304.11
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