%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