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Data"],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor (\n            <jats:sans-serif>USE<\/jats:sans-serif>\n            ), which does not require any side information and rigid assumptions on data. Furthermore,\n            <jats:sans-serif>USE<\/jats:sans-serif>\n            can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of\n            <jats:sans-serif>USE<\/jats:sans-serif>\n            is well justified mathematically, and we also empirically demonstrate the effectiveness of\n            <jats:sans-serif>USE<\/jats:sans-serif>\n            for several benchmark datasets.\n          <\/jats:p>","DOI":"10.1145\/3459082","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T21:06:18Z","timestamp":1626815178000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Subspace Extraction via Deep Kernelized Clustering"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-0782","authenticated-orcid":false,"given":"Gyoung S.","family":"Na","sequence":"first","affiliation":[{"name":"Korea Research Institute of Chemical Technology, Gajeong-ro, Yuseong-gu, Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunju","family":"Chang","sequence":"additional","affiliation":[{"name":"Korea Research Institute of Chemical Technology, Gajeong-ro, Yuseong-gu, Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1401542"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385654"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045796.3045801"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045796.3045801"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718501"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2649387.2649442"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008641220268"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 2015 10th International Conference on Intelligent Systems: Theories and Applications1\u20136. 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