{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T15:55:44Z","timestamp":1758124544165,"version":"3.41.0"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0105702"],"award-info":[{"award-number":["2020AAA0105702"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61572240, 61872424, 6193000388, U19B2038"],"award-info":[{"award-number":["61572240, 61872424, 6193000388, U19B2038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013058","name":"Primary Research and Development Plan of Jiangsu Province","doi-asserted-by":"crossref","award":["BE2018627"],"award-info":[{"award-number":["BE2018627"]}],"id":[{"id":"10.13039\/501100013058","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            Cross-domain data has become very popular recently since various viewpoints and different sensors tend to facilitate better data representation. In this article, we propose a novel\n            <jats:bold>cross-domain object representation algorithm (RLRCA)<\/jats:bold>\n            which not only explores the complexity of multiple relationships of variables by\n            <jats:bold>canonical correlation analysis (CCA)<\/jats:bold>\n            but also uses a low rank model to decrease the effect of noisy data. To the best of our knowledge, this is the first try to smoothly integrate CCA and a low-rank model to uncover correlated components across different domains and to suppress the effect of noisy or corrupted data. In order to improve the flexibility of the algorithm to address various cross-domain object representation problems, two instantiation methods of RLRCA are proposed from feature and sample space, respectively. In this way, a better cross-domain object representation can be achieved through effectively learning the intrinsic CCA features and taking full advantage of cross-domain object alignment information while pursuing low rank representations. Extensive experimental results on CMU PIE, Office-Caltech, Pascal VOC 2007, and NUS-WIDE-Object datasets, demonstrate that our designed models have superior performance over several state-of-the-art cross-domain low rank methods in image clustering and classification tasks with various corruption levels.\n          <\/jats:p>","DOI":"10.1145\/3458825","type":"journal-article","created":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T21:16:06Z","timestamp":1636751766000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Cross-Domain Object Representation via Robust Low-Rank Correlation Analysis"],"prefix":"10.1145","volume":"17","author":[{"given":"Xiangjun","family":"Shen","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu, China"}]},{"given":"Jinghui","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu, China"}]},{"given":"Zhongchen","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu, China"}]},{"given":"Bingkun","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China"}]},{"given":"Zhengjun","family":"Zha","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}]}],"member":"320","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"A kernel method for canonical correlation analysis","author":"Akaho Shotaro","year":"2006","unstructured":"Shotaro Akaho. 2006. 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