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In recent years, many semi-supervised nonnegative matrix factorization algorithms were proposed by considering label information, which has achieved outstanding performance for multi-view clustering. However, most of these existing methods have either failed to consider discriminative information effectively or included too much hyper-parameters. Addressing these issues, a semi-supervised multi-view nonnegative matrix factorization with a novel fusion regularization (FRSMNMF) is developed in this article. In this work, we uniformly constrain alignment of multiple views and discriminative information among clusters with designed fusion regularization. Meanwhile, to align the multiple views effectively, two kinds of compensating matrices are used to normalize the feature scales of different views. Additionally, we preserve the geometry structure information of labeled and unlabeled samples by introducing the graph regularization simultaneously. Due to the proposed methods, two effective optimization strategies based on multiplicative update rules are designed. Experiments implemented on six real-world datasets have demonstrated the effectiveness of our FRSMNMF comparing with several state-of-the-art unsupervised and semi-supervised approaches.<\/jats:p>","DOI":"10.1145\/3653022","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T10:26:52Z","timestamp":1710757612000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Semi-supervised Multi-view Clustering based on NMF with Fusion Regularization"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6085-3126","authenticated-orcid":false,"given":"Guosheng","family":"Cui","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,  Shenzhen, China and Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4772-3284","authenticated-orcid":false,"given":"Ruxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5838-0198","authenticated-orcid":false,"given":"Dan","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5351-8546","authenticated-orcid":false,"given":"Ye","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2315571"},{"key":"e_1_3_3_3_2","first-page":"19","volume-title":"Proceedings of the 2004 SIAM International Conference on Data Mining","author":"Bickel Steffen","year":"2004","unstructured":"Steffen Bickel and Tobias Scheffer. 2004. 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