{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:28:46Z","timestamp":1762352926798,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National key R&D Program of China","doi-asserted-by":"crossref","award":["2019YFA0706200"],"award-info":[{"award-number":["2019YFA0706200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61806035, U1936217, 61702156, 61772171, and 61876056"],"award-info":[{"award-number":["61806035, U1936217, 61702156, 61772171, and 61876056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Anhui Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["1808085QF188"],"award-info":[{"award-number":["1808085QF188"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,6,23]]},"abstract":"<jats:p>Multi-task learning has been widely applied to Alzheimer\u2019s 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\u2019s disease data.<\/jats:p>","DOI":"10.1145\/3398728","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:39:34Z","timestamp":1594125574000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Adaptive Multi-Task Dual-Structured Learning with Its Application on Alzheimer\u2019s Disease Study"],"prefix":"10.1145","volume":"21","author":[{"given":"Shijie","family":"Hao","sequence":"first","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), China, and Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), China, and Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), China, and Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanrong","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), China, and Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), China, and Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"For the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"volume-title":"2018 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s & Dementia 14","year":"2018","author":"Alzheimer\u2019s Association","key":"e_1_2_1_1_1"},{"volume":"11070","volume-title":"21st International Conference, Proceedings, Part I (MICCAI \u201918)","author":"Brand Lodewijk","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.07.018"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.085"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2545658"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3016100.3016134"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2885331"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2553663"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.09.015"},{"key":"e_1_2_1_10_1","volume-title":"20th International Conference, Proceedings, Part III (MICCAI \u2019 17)","volume":"10435","author":"Liu Mingxia","year":"2017"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2869989"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2997046.2997098"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2520964"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2847685"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298595"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298595"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-014-9238-1"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.08.002"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2017.2743219"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.03.051"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.12.029"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354742"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126288"},{"volume-title":"matrix norm and its application in feature selection. CoRR abs\/1303.3987","year":"2013","author":"Wang Liping","key":"e_1_2_1_24_1"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.007"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.07.009"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2457339"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2777489"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3060832.3060922"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878970"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2872061"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.069"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2688363"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2403356"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.05.078"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.10.008"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2763618"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2785795"}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3398728","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3398728","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:31Z","timestamp":1750199611000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3398728"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6,23]]}},"alternative-id":["10.1145\/3398728"],"URL":"https:\/\/doi.org\/10.1145\/3398728","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"type":"print","value":"1533-5399"},{"type":"electronic","value":"1557-6051"}],"subject":[],"published":{"date-parts":[[2021,5,24]]},"assertion":[{"value":"2020-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}