(2006). The fastest algorithm for analyzing robust principal components with application on variables affecting the increase of aluminum level in blood. , 0(20), 36-55.
. "The fastest algorithm for analyzing robust principal components with application on variables affecting the increase of aluminum level in blood". , 0, 20, 2006, 36-55.
(2006). 'The fastest algorithm for analyzing robust principal components with application on variables affecting the increase of aluminum level in blood', , 0(20), pp. 36-55.
The fastest algorithm for analyzing robust principal components with application on variables affecting the increase of aluminum level in blood. , 2006; 0(20): 36-55.

The fastest algorithm for analyzing robust principal components with application on variables affecting the increase of aluminum level in blood

Al-Rafidain University College For Sciences
Article 1, Volume 0, Issue 20, 2017, Pages 36-55
Abstract
One of the common techniques in analyzing multivariate data is analyzing of the principal components. This transforms large number of related variables into lesser number of no related components (PCA).
In case of existence of outliers, which can be detected in several ways, then
the dependence of variance and ordinary covariance matrices, also correlation matrix, would lead to misleading results in analyzing the principal components.
The aim of this research is to introduce a new and fast algorithm in analyzing the robust principal components; when data contain outliers, while conventional methods fall in detecting outliers in data; then the results are misleading. The method is implemented to show its real effectiveness on variables affecting the increase of aluminum level in blood.
Keywords
algorithm
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