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Robust Classification of Principal Component Analysis by Using Robust Soft Independent Modeling of Class Analogies Method | ||
| Al-Ma'mon College Journal | ||
| Article 1, Volume 0, Issue 25, 2018, Pages 151-165 | ||
| Author | ||
| Sura S. Kteeb | ||
| Abstract | ||
| This paper deals with the robust method to classify high-dimensional observations which is called RSIMCA method. This robust method is based on Principal Component Analysis (PCA) for high-dimensional data. Classical PCA constructs a set of uncorrelated variables which correspond to eigen vectors of the sample covariance or correlation matrix. This covariance matrix is strongly affected by outliers observations, therefore , it is necessary to use robust methods that are resistant to outliers. The application has been through various simulation experiments .The results showed the efficiency of the RSIMCA method. | ||
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