A Proposal for Robpca Algorithm


Abstract


Principal component Analysis (PCA) is one of the most frequently used multivariate statistical methods. Especially, it is used on the purpose of dimension reduction and obtaining uncorrelated variables. However, classic PCA (CPCA) is sensitive to outlier. Because it is based on classic covariance or correlation matrices influenced by outliers. Therefore, CPCA can give fallacious results in data sets which have outliers. In this study, the robust PCA (RPCA) methods to solve this problem of CPCA are introduced in literature. Moreover, we bring forward a proposal to ROBPCA algorithm which is one of these methods.


Keywords


ROBPCA; Robust Principal Component Analysis; Standardization; High Dimensional Data.

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References


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