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• W4207006620 abstract "Smart healthcare systems are generating a large scale of heterogenous high-dimensional data with complex relationships. It is hard for current methods to analyze such high-dimensional healthcare data. Specifically, the traditional data reduction methods can not keep the correlation among different modalities of data objects, while the latest methods based on tensor singular value decomposition are not effective for data reduction, although they can keep the correlation. This article presents a tensor train-tensor singular value decomposition (TT-TSVD) algorithm for data reduction. Particularly, the presented algorithm balances the correlation-preservation ability of modalities and data reduction ability by combining the advantages of the train structure of the tensor train decomposition and the association relationship between the tensor singular value decomposition retention mode. Extensive experiments are conducted on the convolutional neural network and the results clearly show that the presented algorithm performs effectively for data reduction with a low-loss classification accuracy; what is more, classification accuracy on medical image dataset has been improved a little." @default.
• W4207006620 created "2022-01-26" @default.
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• W4207006620 date "2022-01-25" @default.
• W4207006620 modified "2023-09-25" @default.
• W4207006620 title "TT-TSVD: A Multi-modal Tensor Train Decomposition with Its Application in Convolutional Neural Networks for Smart Healthcare" @default.
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• W4207006620 doi "https://doi.org/10.1145/3491223" @default.
• W4207006620 hasPublicationYear "2022" @default.
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