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Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In order to resolve the previously mentioned problem, a new method is proposed, which builds on the approach of low-rank representation. The proposed approach first learns a low-rank coefficient matrix from data by exploiting the data\u2019s self-expressiveness property. Then, a regularization term is introduced to ensure that the representation coefficient of two samples, which are similar in original high-dimensional space, is close to maintaining the samples\u2019 neighborhood structure in the low-dimensional space. As a result, the proposed method obtains a clustering structure directly through the low-rank coefficient matrix to guarantee optimal clustering performance. A wide range of experiments shows that the proposed method is superior to compared state-of-the-art methods.<\/jats:p>","DOI":"10.1155\/2022\/7540956","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T14:52:22Z","timestamp":1651243942000},"page":"1-11","source":"Crossref","is-referenced-by-count":3,"title":["Robust Spectral Clustering via Low-Rank Sample Representation"],"prefix":"10.1155","volume":"2022","author":[{"given":"Hao","family":"Liang","sequence":"first","affiliation":[{"name":"Graduate School of Jiangsu University, Zhenjiang 212013, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai-Tang","family":"Guan","sequence":"additional","affiliation":[{"name":"Haian Experimental High School, Nantong, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9509-1915","authenticated-orcid":true,"given":"Stanley Ebhohimhen","family":"Abhadiomhen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China"},{"name":"Department of Computer Science, University of Nigeria, Nsukka, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"An efficient k-means clustering algorithm","volume":"43","author":"K. 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