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Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph\u2010based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction\u2010based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self\u2010expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce <jats:italic>l<\/jats:italic><jats:sub>2,1<\/jats:sub>\u2010norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1155\/2021\/9458188","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T19:35:09Z","timestamp":1624649709000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Robust Graph Structure Learning for Multimedia Data Analysis"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5931-3197","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxuan","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4703-780X","authenticated-orcid":false,"given":"Shaojie","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2892971"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.03.024"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-018-0287-5"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.05.023"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107522"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2020.115984"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107627"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.06.067"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.015"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2009.2038764"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.04.029"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"ZhangL. 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