{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:13:31Z","timestamp":1750306411682,"version":"3.41.0"},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2015,2,5]],"date-time":"2015-02-05T00:00:00Z","timestamp":1423094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Program on Key Basic Research Project"},{"name":"Singapore National Research Foundation under its International Research Centre@Singapore Funding Initiative"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61373122"],"award-info":[{"award-number":["61373122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"IDM Programme Office"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2015,2,5]]},"abstract":"<jats:p>Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.<\/jats:p>","DOI":"10.1145\/2700286","type":"journal-article","created":{"date-parts":[[2015,2,10]],"date-time":"2015-02-10T13:19:47Z","timestamp":1423574387000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Boosted Multifeature Learning for Cross-Domain Transfer"],"prefix":"10.1145","volume":"11","author":[{"given":"Xiaoshan","family":"Yang","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences and China-Singapore Institute of Digital Media, Singapore"}]},{"given":"Tianzhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences and China-Singapore Institute of Digital Media, Singapore"}]},{"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences and China-Singapore Institute of Digital Media, Singapore"}]},{"given":"Ming-Hsuan","family":"Yang","sequence":"additional","affiliation":[{"name":"University of California, Merced, CA"}]}],"member":"320","published-online":{"date-parts":[[2015,2,5]]},"reference":[{"volume-title":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 60--75","author":"Al-Stouhi Samir","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461466.2461491"},{"volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems. 137--144","year":"2006","author":"Ben-David Shai","key":"e_1_2_1_3_1"},{"volume-title":"Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 129--136","year":"2010","author":"Ben-David Shai","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems. 181--189","year":"2010","author":"Bergamo Alessandro","key":"e_1_2_1_6_1"},{"volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems.","year":"2007","author":"Blitzer John","key":"e_1_2_1_7_1"},{"key":"e_1_2_1_8_1","first-page":"173","article-title":"Domain adaptation with coupled subspaces","volume":"15","author":"Blitzer John","year":"2011","journal-title":"J. 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