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Adaptive Multi-Task Dual-Structured Learning with Its Application on Alzheimer’s Disease Study

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Published:24 May 2021Publication History
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Abstract

Multi-task learning has been widely applied to Alzheimer’s Disease (AD) studies due to its capability of simultaneously rating the disease severity (classification) and predicting corresponding clinical scores (regression). In this article, we propose a novel technique of Adaptive Multi-task Dual-Structured Learning, named AMDSL, by mutually exploring the dual manifold structure for the label and regression score of the disease data under joint classification and regression tasks, while learning an adaptive shared similarity measure and corresponding feature mapping among these two tasks. We encode both the reconstructed label representation and regression score adaptive to the ideal similarity measure on disease data to achieve the ideal performance on these two joint tasks. The alternating algorithm is proposed to optimize the above objective. We theoretically prove the convergence of the optimization algorithm. The superiority of AMDSL is experimentally validated under joint classification and regression as per various evaluation metrics against the most authoritative Alzheimer’s disease data.

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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 21, Issue 2
        June 2021
        599 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3453144
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 May 2021
        • Online AM: 16 May 2020
        • Accepted: 1 May 2020
        • Revised: 1 April 2020
        • Received: 1 March 2020
        Published in toit Volume 21, Issue 2

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