| dc.description.abstract | Contrastive Principal Component Analysis (cPCA) is a useful dimensionality reduction technique under some specific scenarios in which datasets are collected under different conditions, e.g., a treatment and a control experiment, especially in visualizing and exploring patterns that are specific to one dataset. In this study, we propose a new methodology to deal with cPCA in high-dimension, low-sample-size (HDLSS) data situations. The proposed method, called cPCA-NR, gives an idea of applying the noise-reduction (NR) method proposed by Yata and Aoshima (2012) to mitigates the adverse effects of noisy data points, improving the robustness and reliability of the dimensionality reduction process. In simulation study, we demonstrate that the cPCA-NR outperforms traditional PCA in terms of classification accuracy and clustering performance. Moreover, the proposed method exhibits strong resilience to noisy data, achieving notable improvements in scenarios with high levels of noise. The results highlight the superior performance of cPCA-NR, establishing its potential as a valuable tool for various applications, such as image recognition, anomaly detection, and data visualization. | en_US |