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Existing UFS methods implicitly assigned the same attribute score to each sample, which disregarded the distinctiveness of features and weakened the clustering performance of UFS methods to some extent. To alleviate these issues, a novel UFS method is proposed, named unsupervised feature selection with latent relationship penalty term (LRPFS). Firstly, latent learning is innovatively designed by assigning explicitly an attribute score to each sample according to its unique importance in clustering results. With this strategy, the inevitable noise interference can be removed effectively while retaining the intrinsic structure of data samples. Secondly, an appropriate sparse model is incorporated into the penalty term to further optimize its roles as follows: (1) It imposes potential constraints on the feature matrix to guarantee the uniqueness of the solution. (2) The interconnection between data instances is established by a pairwise relationship situation. Extensive experiments on benchmark datasets demonstrate that the proposed method is superior to relevant state-of-the-art algorithms with an average improvement of 10.17% in terms of accuracy.<\/jats:p>","DOI":"10.3390\/axioms13010006","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T03:36:17Z","timestamp":1703129777000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Unsupervised Feature Selection with Latent Relationship Penalty Term"],"prefix":"10.3390","volume":"13","author":[{"given":"Ziping","family":"Ma","sequence":"first","affiliation":[{"name":"School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0366-7509","authenticated-orcid":false,"given":"Yulei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huirong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Application, Shangluo University, Shangluo 726000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/34.574797","article-title":"Feature selection: Evaluation, application, and small sample performance","volume":"19","author":"Jain","year":"1997","journal-title":"IEEE Trans. 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