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Although many traditional clustering methods have been proposed for tumor sample clustering, advanced algorithms with better performance are still needed. Low\u2010rank subspace clustering is a popular algorithm in recent years. In this paper, we propose a novel one\u2010step robust low\u2010rank subspace segmentation method (ORLRS) for clustering the tumor sample. For a gene expression data set, we seek its lowest rank representation matrix and the noise matrix. By imposing the discrete constraint on the low\u2010rank matrix, without performing spectral clustering, ORLRS learns the cluster indicators of subspaces directly, i.e., performing the clustering task in one step. To improve the robustness of the method, capped norm is adopted to remove the extreme data outliers in the noise matrix. Furthermore, we conduct an efficient solution to solve the problem of ORLRS. Experiments on several tumor gene expression data demonstrate the effectiveness of ORLRS.<\/jats:p>","DOI":"10.1155\/2021\/9990297","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T05:35:28Z","timestamp":1639028128000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["One\u2010Step Robust Low\u2010Rank Subspace Segmentation for Tumor Sample Clustering"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0470-924X","authenticated-orcid":false,"given":"Jian","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhu","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5327-1088","authenticated-orcid":false,"given":"Xuesong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuguang","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.bioeng.4.020702.153438"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0308531101"},{"key":"e_1_2_8_3_2","article-title":"Robust principal component analysis?","volume":"58","author":"Candes E. 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