@article{(Open Science Index):https://publications.waset.org/pdf/5605, title = {Application of Multi-Dimensional Principal Component Analysis to Medical Data}, author = {Naoki Yamamoto and Jun Murakami and Chiharu Okuma and Yutaro Shigeto and Satoko Saito and Takashi Izumi and Nozomi Hayashida}, country = {}, institution = {}, abstract = {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.}, journal = {International Journal of Computer and Information Engineering}, volume = {6}, number = {3}, year = {2012}, pages = {280 - 286}, ee = {https://publications.waset.org/pdf/5605}, url = {https://publications.waset.org/vol/63}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 63, 2012}, }