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TT-TSVD: A Multi-modal Tensor Train Decomposition with Its Application in Convolutional Neural Networks for Smart Healthcare

Published:25 January 2022Publication History
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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.

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 1s
        February 2022
        352 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/3505206
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 January 2022
        • Accepted: 1 October 2021
        • Revised: 1 July 2021
        • Received: 1 November 2020
        Published in tomm Volume 18, Issue 1s

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