Nisa, Khoirin and Herawati, Netti and Setiawan, Eri and Nusyirwan, Nusyirwan (2006) Robust Principal Component Analysis Using Minimum Covariance Determinant Estimator. Proceeding of International Conference on Mathematics and Natural Sciences.

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Principal component analysis (PCA) is one of the most widely used statistical techniques for data dimension reduction. And a robust technique on PCA when data contains outlier is an important problem. In this paper, we consider the problem of robust PCA based on Minimum Covariance Determinant (MCD) mean and covariance matrix estimator. We aimed to look at the behavior of the principal component scores of robust PCA using MCD through their graphical plot of the first and the second PCS. The effect of outliers on PCA is studied by comparing the mean square error (MSE) resulted by MCD and by classical method in presence of outliers.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Depositing User: DR. KHOIRIN NISA
Date Deposited: 27 Mar 2018 04:46
Last Modified: 27 Mar 2018 04:46

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