The Recognition-Taguchi (RT) method has been proposed for evaluating binary data such as image data. Additionally, the RT-PC method can be applied to continuous-type data when the variables have different units. The RT-PC method uses the principal component analysis (PCA); however, applying PCA to high-dimensional data causes complications in the estimation accuracy of the eigenvalue and eigenvector. Therefore, a method based on the sparse PCA is proposed in this context instead of the conventional PCA. However, previous studies have not investigated the assumption that the anomaly-detection performance of the RT-PC method is based on a typical high-dimensional PCA. In this study, we introduce the noise-reduction and cross-data (CD) matrix methods to the RT-PC method and evaluate their performance using Monte Carlo simulation. The RT-CD method uses the calculation process of the CD matrix method instead of PCA. The simulation analysis results show a better performance in the pattern of inner anomalies when compared to the RT-PC method. The RT-NR method uses the calculation process of the noise-reduction method in the RT-PC method instead of PCA. Finally, the RT-NR and RT-PC methods are observed to exhibit the same anomaly-detection performance.