{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:25:58Z","timestamp":1760955958613,"version":"3.41.0"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T00:00:00Z","timestamp":1564358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Scientific Research Common Program of Beijing Municipal Commission of Education","award":["KM201710005022 and KM201510005024"],"award-info":[{"award-number":["KM201710005022 and KM201510005024"]}]},{"name":"Beijing Natural Science Foundation","award":["4172003 and 4184082"],"award-info":[{"award-number":["4172003 and 4184082"]}]},{"name":"University of Sydney Business School ARC Bridging Fund"},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772048, 61672071, U1811463, 61632006, 61876012, and 61806014"],"award-info":[{"award-number":["61772048, 61672071, U1811463, 61632006, 61876012, and 61806014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2019,8,31]]},"abstract":"<jats:p>Dimensionality reduction is widely used to deal with high-dimensional data. As a famous dimensionality reduction method, principal component analysis (PCA) aiming at finding the low dimension feature of original data has made great successes, and many improved PCA algorithms have been proposed. However, most algorithms based on PCA only consider the linear correlation of data features. In this article, we propose a novel dimensionality reduction model called maximally correlated PCA based on deep parameterization learning (MCPCADP), which takes nonlinear correlation into account in the deep parameterization framework for the purpose of dimensionality reduction. The new model explores nonlinear correlation by maximizing Ky-Fan norm of the covariance matrix of nonlinearly mapped data features. A new BP algorithm for model optimization is derived. In order to assess the proposed method, we conduct experiments on both a synthetic database and several real-world databases. The experimental results demonstrate that the proposed algorithm is comparable to several widely used algorithms.<\/jats:p>","DOI":"10.1145\/3332183","type":"journal-article","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T20:55:51Z","timestamp":1564433751000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Maximally Correlated Principal Component Analysis Based on Deep Parameterization Learning"],"prefix":"10.1145","volume":"13","author":[{"given":"Haoran","family":"Chen","sequence":"first","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"given":"Jinghua","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"given":"Junbin","family":"Gao","sequence":"additional","affiliation":[{"name":"The University of Sydney, NSW, Australia"}]},{"given":"Yanfeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"given":"Yongli","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, Beijing, China"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]}],"member":"320","published-online":{"date-parts":[[2019,7,29]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2638581"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2910585"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.04.025"},{"key":"e_1_2_2_4_1","first-page":"580","article-title":"Non-linear dimensionality reduction","volume":"5","author":"DeMers D.","year":"1993","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_5_1","unstructured":"S. Feizi and D. Tse. 2017. Maximally correlated principal component analysis. arXiv:1702.05471v2.  S. Feizi and D. Tse. 2017. Maximally correlated principal component analysis. arXiv:1702.05471v2."},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3046948"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2935750"},{"volume":"6312","volume-title":"European Conference on Computer Vision","author":"Ghanem B.","key":"e_1_2_2_8_1"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2009.08.002"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.55"},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"G. E. Hinton and R. R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313 5786 (2006) 504--507.  G. E. Hinton and R. R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313 5786 (2006) 504--507.","DOI":"10.1126\/science.1127647"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.07.009"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/0954-898X_11_3_302"},{"volume-title":"IEEE Conference on Computer Vision and Pattern Recognition. 511--517","author":"Ke Y.","key":"e_1_2_2_14_1"},{"volume-title":"Proceeding of the International Conference on Learning Representations.","author":"Kingma D. P.","key":"e_1_2_2_15_1"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-69497-7_27"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2004.11.042"},{"key":"e_1_2_2_18_1","doi-asserted-by":"crossref","unstructured":"J. A. Lee and M. Verleysen. 2007. Nonlinear Dimensionality Reduction. Springer Science and Business Media.   J. A. Lee and M. Verleysen. 2007. Nonlinear Dimensionality Reduction. Springer Science and Business Media.","DOI":"10.1007\/978-0-387-39351-3"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.860853"},{"key":"e_1_2_2_20_1","first-page":"536","article-title":"Kernel PCA and de-noising in feature spaces","volume":"11","author":"Mika S.","year":"1998","journal-title":"Advances in Neural Information Processing Systems"},{"volume-title":"Machine Learning: A Probabilistic Perspective","year":"2012","author":"Murphy K. P.","key":"e_1_2_2_21_1"},{"volume-title":"Technical Report","year":"1890","author":"Ng A.","key":"e_1_2_2_22_1"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspl.1895.0041"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1969.222678"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30499-9_161"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/646257.685385"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2501810"},{"key":"e_1_2_2_30_1","first-page":"841","article-title":"Automatic alignment of hidden representations","volume":"15","author":"Teh Y. W.","year":"2002","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2015.78"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015345"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1839490.1839495"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1110"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3332183","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3332183","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:18Z","timestamp":1750202598000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3332183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,29]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,8,31]]}},"alternative-id":["10.1145\/3332183"],"URL":"https:\/\/doi.org\/10.1145\/3332183","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2019,7,29]]},"assertion":[{"value":"2018-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-07-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}