Robust Singular Value Decomposition Method on Minor Outlier Data

Bernadhita Herindri Samodera Utami (STMIK Pringsewu)
Trisnawati Trisnawati (STMIK Pringsewu)
Rani Pratiwi (STMIK Pringsewu)
Miswan Gumanti (STMIK Pringsewu)

Jurnal Varian
Vol 4 No 1 (2020)

In multivariate statistics, Singular Value Decomposition (SVD) for a data matrix containing outliers does not provide data that can be analyzed optimally. This study aims to overcome outlier data using the Robust Singular Value Decomposition (RSVD) method and compare it with the SVD method. The analysis using the RSVD method includes several steps, namely determining the initial predictive value of the vector u and regressing it then normalizing the estimator vector β and carrying out the iteration process until convergent results are obtained. The results of this study indicate that the RSVD for dealing with minor outliers data is not influenced by initial estimates. The RSVD method is strongly influenced by the large amount of outliers data, the more extreme outliers data, the more iterations are.