{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:32:33Z","timestamp":1769567553288,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:p>With the rapid advancement of AI and sensor technologies, high-dimensional unlabeled data are increasingly common in areas like healthcare and the Internet of Things. Unsupervised feature selection is crucial for reducing dimensionality while preserving information, yet existing multi-scale methods often use fixed parameters, limiting their adaptability to hierarchical data structures and dynamic feature relationships. To overcome this, we propose a Hierarchical Granularity-Optimized Feature Selection (HGUMFS) algorithm. It uses the Hopkins statistic to dynamically determine data structure and constructs a recursive spectral clustering hierarchy, along with a dual-modal fuzzy similarity mechanism and decay-weighted feature fusion to identify optimal subsets. Evaluations on public datasets show that HGUMFS improves clustering accuracy and feature reduction rates over existing methods.<\/jats:p>","DOI":"10.3233\/faia251646","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:18:46Z","timestamp":1769519926000},"source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Unsupervised Feature Selection with Hierarchical Granularity Optimization"],"prefix":"10.3233","author":[{"given":"Hongwu","family":"Qin","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, China"}]},{"given":"An","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, China"}]},{"given":"Xiuqin","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, China"}]},{"given":"Keqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251646","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:18:46Z","timestamp":1769519926000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251646","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}