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Appl."],"published-print":{"date-parts":[[2021,1,31]]},"abstract":"<jats:p>Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.1145\/3408318","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T01:53:55Z","timestamp":1617242035000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["Gaussian Mixture Model Clustering with Incomplete Data"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8700-7712","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer, NUDT, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miaomiao","family":"Li","sequence":"additional","affiliation":[{"name":"Changsha University; School of Computer, NUDT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, NUDT, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sisi","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer, NUDT, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer, NUDT, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"En","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer, NUDT, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiying","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University; Department of Computer Science, City University of Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoyun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, NUDT, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Clustering with deep learning: Taxonomy and new methods. arXiv: Learning","author":"Aljalbout Elie","year":"2018","unstructured":"Elie Aljalbout , Vladimir Golkov , Yawar Siddiqui , and Daniel Cremers . 2018. 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