%0 Journal Article %A Naoki Yamamoto and Jun Murakami and Chiharu Okuma and Yutaro Shigeto and Satoko Saito and Takashi Izumi and Nozomi Hayashida %D 2012 %J International Journal of Computer and Information Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 63, 2012 %T Application of Multi-Dimensional Principal Component Analysis to Medical Data %U https://publications.waset.org/pdf/5605 %V 63 %X Multi-dimensional principal component analysis (PCA) is the extension of the PCA, which is used widely as the dimensionality reduction technique in multivariate data analysis, to handle multi-dimensional data. To calculate the PCA the singular value decomposition (SVD) is commonly employed by the reason of its numerical stability. The multi-dimensional PCA can be calculated by using the higher-order SVD (HOSVD), which is proposed by Lathauwer et al., similarly with the case of ordinary PCA. In this paper, we apply the multi-dimensional PCA to the multi-dimensional medical data including the functional independence measure (FIM) score, and describe the results of experimental analysis. %P 280 - 286