@article{(International 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 	= {International Science Index 63, 2012},
	}