{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:28:41Z","timestamp":1740461321353,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Traditional clustering methods cluster data with pairwise graph and usually result in information loss. In this paper, we propose a novel spectral clustering method by combing hypergraph and sample self-representation together. Specially, the proposed algorithm employs sample self-representation based loss function &amp;ell;2,1-norm which is row sparse to weaken the effects of the noises. And then, a hypergraph regular term is imposed to construct the hypergraph Laplacian which fully consider the complex similarity relationships of the data. The experimental results on benchmark data-sets indicated that the proposed algorithm prominently outperforms the compared state-of-the-art algorithms in terms of CE, such as SRC, LSR and et al.<\/jats:p>","DOI":"10.3233\/978-1-61499-722-1-334","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:58:24Z","timestamp":1740398304000},"source":"Crossref","is-referenced-by-count":0,"title":["Hypergraph Spectral Clustering via Sample Self-Representation"],"prefix":"10.3233","author":[{"family":"Zhang Shi-Chao","sequence":"additional","affiliation":[]},{"family":"Li Yong-Gang","sequence":"additional","affiliation":[]},{"family":"Cheng De-Bo","sequence":"additional","affiliation":[]},{"family":"Deng Zhen-Yun","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining II"],"original-title":[],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:22:30Z","timestamp":1740399750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-721-4&spage=334&doi=10.3233\/978-1-61499-722-1-334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-722-1-334","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}