2024 Volume 10 Issue 1 Pages 1-7
The RT method, one of the methods of the Mahalanobis-Taguchi (MT) system, has been applied to various fields. The RT-PC method is another RT method that incorporate principal component analysis. This method can be applied to data set consisting of variables that do not have the same dimensions.
This study proposes an RT-PC method that incorporate sparse principal component analysis (SPCA) as an anomaly detection method for small-sample data. SPCA incorporates L1 regularization term into traditional principal component analysis, where an appropriate choice of L1 regularization term improves the accuracy of anomaly detection for sparse data and reduces the number of nonzero elements in the eigenvectors, thereby increasing interpretability. Numerical experiments have confirmed that the accuracy of the proposed procedure is similar to or better than conventional procedure, and we have confirmed that an RT-PC method that incorporate SPCA reduces the number of nonzero elements in the eigenvectors more than RT-PC method. This is thought to improve interpretability of principal component.
