{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T21:15:37Z","timestamp":1790457337319,"version":"4.1.0"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T00:00:00Z","timestamp":1621382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,28]]},"abstract":"<jats:p>\n            The online analysis of multi-way data stored in a tensor\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.\n          <\/jats:p>","DOI":"10.1145\/3451217","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T17:08:53Z","timestamp":1621444133000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Online Tensor-Based Learning Model for Structural Damage Detection"],"prefix":"10.1145","volume":"15","author":[{"given":"Ali","family":"Anaissi","sequence":"first","affiliation":[{"name":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Basem","family":"Suleiman","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seid Miad","family":"Zandavi","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489383"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57454-7_4"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70087-8_51"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0151-9"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04182-3_54"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 1257\u20131264","author":"Anaissi Ali","year":"2020","unstructured":"Ali Anaissi , Basem Suleiman , and Seid Miad Zandavi . 2020 . Online tensor decomposition with optimized stochastic gradient descent: An application in structural damage identification . In Proceedings of 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 1257\u20131264 . Ali Anaissi, Basem Suleiman, and Seid Miad Zandavi. 2020. Online tensor decomposition with optimized stochastic gradient descent: An application in structural damage identification. In Proceedings of 2020 IEEE Symposium Series on Computational Intelligence. 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IEEE, 2685\u20132690."},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of The 28th Conference on Learning Theory. PMLR, 797\u2013842","author":"Ge Rong","year":"2015","unstructured":"Rong Ge , Furong Huang , Chi Jin , and Yang Yuan . 2015 . Escaping from saddle points\u2014online stochastic gradient for tensor decomposition . In Proceedings of The 28th Conference on Learning Theory. PMLR, 797\u2013842 . Rong Ge, Furong Huang, Chi Jin, and Yang Yuan. 2015. Escaping from saddle points\u2014online stochastic gradient for tensor decomposition. In Proceedings of The 28th Conference on Learning Theory. PMLR, 797\u2013842."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-015-0871-8"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2190406"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305559"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132851"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248616"},{"key":"e_1_2_1_18_1","unstructured":"Vadim Lebedev Yaroslav Ganin Maksim Rakhuba Ivan Oseledets and Victor Lempitsky. 2014. Speeding-up convolutional neural networks using fine-tuned CP-decomposition. arXiv:1412.6553 . Retrieved from https:\/\/arxiv.org\/abs\/1412.6553.  Vadim Lebedev Yaroslav Ganin Maksim Rakhuba Ivan Oseledets and Victor Lempitsky. 2014. Speeding-up convolutional neural networks using fine-tuned CP-decomposition. arXiv:1412.6553 . 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