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Application of Multi-Dimensional Principal Component Analysis to Medical Data

Authors: Chiharu Okuma, Jun Murakami, Naoki Yamamoto, Yutaro Shigeto, Satoko Saito, Takashi Izumi, Nozomi Hayashida


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.

Keywords: Medical Data, multi-dimensional principal component analysis, higher-order SVD (HOSVD), functional independence measure (FIM), tensor decomposition

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