{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:34:11Z","timestamp":1777037651868,"version":"3.51.4"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong Research Grants Council (RGC) - General Research Fund","award":["16216119"],"award-info":[{"award-number":["16216119"]}]},{"name":"National Science Foundation (NSF) - Civil, Mechanical and Manufacturing Innovation","award":["1922739"],"award-info":[{"award-number":["1922739"]}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","award":["DE-EE0009354"],"award-info":[{"award-number":["DE-EE0009354"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,12,31]]},"abstract":"<jats:p>Anomaly detection is an essential task for quality management in smart manufacturing. An accurate data-driven detection method usually needs enough data and labels. However, in practice, there commonly exist newly set-up processes in manufacturing, and they only have quite limited data available for analysis. Borrowing the name from the recommender system, we call this process a cold-start process. The sparsity of anomaly, the deviation of the profile, and noise aggravate the detection difficulty.<\/jats:p>\n          <jats:p>\n            Transfer learning could help to detect anomalies for cold-start processes by transferring the knowledge from more experienced processes to the new processes. However, the existing transfer learning and multi-task learning frameworks are established on task- or domain-level relatedness. We observe instead, within a domain, some components (background and anomaly) share more commonality, others (profile deviation and noise) not. To this end, we propose a more delicate component-level transfer learning scheme, i.e., decomposition-based hybrid transfer learning (\n            <jats:italic>DHTL<\/jats:italic>\n            ): It first decomposes a domain (e.g., a data source containing profiles) into different components (smooth background, profile deviation, anomaly, and noise); then, each component\u2019s transferability is analyzed by expert knowledge; Lastly, different transfer learning techniques could be tailored accordingly. We adopted the Bayesian probabilistic hierarchical model to formulate parameter transfer for the background, and \u201c\n            <jats:italic>L<\/jats:italic>\n            <jats:sub>2,1<\/jats:sub>\n            +\n            <jats:italic>L<\/jats:italic>\n            <jats:sub>1<\/jats:sub>\n            \u201d-norm to formulate low dimension feature-representation transfer for the anomaly. An efficient algorithm based on Block Coordinate Descend is proposed to learn the parameters. A case study based on glass coating pressure profiles demonstrates the improved accuracy and completeness of detected anomaly, and a simulation demonstrates the fidelity of the decomposition results.\n          <\/jats:p>","DOI":"10.1145\/3530990","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T07:50:29Z","timestamp":1650873029000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4983-9352","authenticated-orcid":false,"given":"Ziyue","family":"Li","sequence":"first","affiliation":[{"name":"University of Cologne and The Hong Kong University of Science and Technology, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4322-7323","authenticated-orcid":false,"given":"Hao","family":"Yan","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, Arizona"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0575-8254","authenticated-orcid":false,"given":"Fugee","family":"Tsung","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7827-1770","authenticated-orcid":false,"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"83","article-title":"Task clustering and gating for bayesian multitask learning","volume":"4","author":"Bakker Bart","year":"2003","unstructured":"Bart Bakker and Tom Heskes. 2003. 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