{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:10:40Z","timestamp":1791483040465,"version":"4.3.4"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/tkde.2019.2935449","type":"journal-article","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T19:43:41Z","timestamp":1566503021000},"page":"1-1","source":"Crossref","is-referenced-by-count":8,"title":["Learning Sparse PCA with Stabilized ADMM Method on Stiefel Manifold"],"prefix":"10.1109","author":[{"given":"Mingkui","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuguang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiezhang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1612","article-title":"Sum-of-squares lower bounds for sparse PCA","author":"ma","year":"2015","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2012.05.003"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.15"},{"key":"ref32","first-page":"517","article-title":"Generalized power method for sparse principal component analysis","volume":"11","author":"journ\u00e9e","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1198\/1061860032148"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.0121"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2819978"},{"key":"ref36","first-page":"1418","article-title":"Fast algorithms for sparse principal component analysis based on rayleigh quotient iteration","author":"kuleshov","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1561\/2000000006"},{"key":"ref34","first-page":"1012","article-title":"Low-rank tensor completion: A Riemannian manifold preconditioning approach","author":"kasai","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref28","first-page":"2036","article-title":"A Riemannian network for SPD matrix learning","author":"huang","year":"2017","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2427451"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1009210544"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1401542"},{"key":"ref1","author":"absil","year":"2009","journal-title":"Optimization Algorithms on Matrix Manifolds"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.531"},{"key":"ref22","first-page":"1529","article-title":"Sparse PCA with oracle property","author":"gu","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref21","first-page":"935","article-title":"Sparse principal component analysis with constraints","author":"grbovic","year":"2012","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.02.005"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2551724"},{"key":"ref26","first-page":"847","article-title":"An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA","author":"hein","year":"2010","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2326001"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v084.i10"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1137\/110845768"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2846588"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1198\/106186006X113430"},{"key":"ref57","first-page":"899","article-title":"Truncated power method for sparse eigenvalue problems","volume":"14","author":"yuan","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref56","first-page":"899","article-title":"Truncated power method for sparse eigenvalue problems","volume":"14","author":"yuan","year":"2013","journal-title":"J Mach Learn Res"},{"key":"ref55","first-page":"149","article-title":"A PCA-based kernel for kernel PCA on multivariate time series","author":"yang","year":"2005","journal-title":"Proc Workshop Temporal Data Mining Algorithms Theory Appl Held Conjunction 5th IEEE Int Conf Data Mining"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.201162998"},{"key":"ref53","first-page":"3383","article-title":"Tighten after relax: Minimax-optimal sparse PCA in polynomial time","author":"wang","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref52","first-page":"2670","article-title":"Fantope projection and selection: A near-optimal convex relaxation of sparse PCA","author":"vu","year":"2013","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref10","first-page":"854","article-title":"Parseval networks: Improving robustness to adversarial examples","author":"cisse","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","first-page":"2859","article-title":"Linear dimensionality reduction: Survey, insights, and generalizations","volume":"16","author":"cunningham","year":"2015","journal-title":"J Mach Learn Res"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOS1097"},{"key":"ref12","first-page":"1269","article-title":"Optimal solutions for sparse principal component analysis","volume":"9","author":"d'aspremont","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1137\/050645506"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2172\/807420"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2002.1183898"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1137\/S0895479895290954"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/7.2.149"},{"key":"ref18","first-page":"4406","article-title":"Towards continuous scientific data analysis and hypothesis evolution","author":"gil","year":"2017","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref19","volume":"3","author":"golub","year":"2012","journal-title":"Matrix Computations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOS664"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.96.12.6745"},{"key":"ref6","first-page":"1455","article-title":"Manopt, a matlab toolbox for optimization on manifolds","volume":"15","author":"boumal","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref5","first-page":"766","article-title":"Sparse PCA via bipartite matchings","author":"asteris","year":"2015","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1214\/18-EJS1450"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/S1535-6108(02)00030-2"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.211566398"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0149"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/nm0102-68"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2007.06.007"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2013.05.099"},{"key":"ref41","first-page":"1017","article-title":"Deflation methods for sparse PCA","author":"mackey","year":"2009","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref44","first-page":"115","article-title":"Multinomial relation prediction in social data: A dimension reduction approach","author":"nori","year":"2012","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref43","first-page":"915","article-title":"Spectral bounds for sparse PCA: Exact and greedy algorithms","author":"moghaddam","year":"2005","journal-title":"Proc Int Conf Neural Inf Process"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/4358933\/08807218.pdf?arnumber=8807218","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:04:39Z","timestamp":1642003479000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8807218\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/tkde.2019.2935449","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}