{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T21:10:08Z","timestamp":1723237808407},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1016\/j.neucom.2018.08.059","type":"journal-article","created":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T12:37:42Z","timestamp":1535805462000},"page":"227-235","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":29,"special_numbering":"C","title":["Spectral clustering based on iterative optimization for large-scale and high-dimensional data"],"prefix":"10.1016","volume":"318","author":[{"given":"Yang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7028-4956","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.neucom.2018.08.059_bib0001","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient graph-based image segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vision"},{"issue":"16","key":"10.1016\/j.neucom.2018.08.059_bib0002","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1016\/j.patrec.2012.07.024","article-title":"Multi-level low-rank approximation-based spectral clustering for image segmentation","volume":"33","author":"Wang","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.neucom.2018.08.059_bib0003","series-title":"Neural Information Processing Systems","first-page":"1813","article-title":"Efficient and robust feature selection via joint l21-norms minimization","author":"Nie","year":"2010"},{"key":"10.1016\/j.neucom.2018.08.059_bib0004","series-title":"Artificial Intelligence","first-page":"1302","article-title":"Unsupervised feature selection with structured graph optimization","author":"Nie","year":"2016"},{"key":"10.1016\/j.neucom.2018.08.059_bib0005","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2018.2828161","article-title":"Optimal clustering framework for hyperspectral band selection","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"7","key":"10.1016\/j.neucom.2018.08.059_bib0006","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1109\/TIP.2010.2044958","article-title":"Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction","volume":"19","author":"Nie","year":"2010","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10.1016\/j.neucom.2018.08.059_bib0007","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s10462-016-9470-1","article-title":"A dimensionality reduction method based on structured sparse representation for face recognition","volume":"46","author":"Gu","year":"2016","journal-title":"Artif. Intell. Rev."},{"issue":"10\u201312","key":"10.1016\/j.neucom.2018.08.059_bib0008","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1016\/j.neucom.2008.12.011","article-title":"Towards understanding hierarchical clustering: a data distribution perspective","volume":"72","author":"Wu","year":"2009","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neucom.2018.08.059_bib0009","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/comjnl\/26.4.354","article-title":"A survey of recent advances in hierarchical clustering algorithms","volume":"26","author":"Murtagh","year":"1983","journal-title":"Comput. J."},{"key":"10.1016\/j.neucom.2018.08.059_bib0010","series-title":"Proceedings of the IEEE Conference on Information & Communication Technologies (ICT)","first-page":"298","article-title":"Review based on data clustering algorithms","author":"Nagpal","year":"2013"},{"key":"10.1016\/j.neucom.2018.08.059_bib0011","series-title":"Advances in neural information processing systems","first-page":"1649","article-title":"Discriminative k-means for clustering","author":"Ye","year":"2008"},{"key":"10.1016\/j.neucom.2018.08.059_bib0012","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.patcog.2017.03.030","article-title":"Locality constraint distance metric learning for traffic congestion detection","volume":"75","author":"Wang","year":"2018","journal-title":"Pattern Recognit."},{"issue":"11","key":"10.1016\/j.neucom.2018.08.059_bib0013","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1109\/TPAMI.2007.1115","article-title":"Weighted graph cuts without eigenvectors a multilevel approach","volume":"29","author":"Dhillon","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.neucom.2018.08.059_bib0014","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1109\/TIT.2014.2346205","article-title":"Improved graph clustering","volume":"60","author":"Chen","year":"2014","journal-title":"IEEE Trans. Inf. Theory"},{"key":"10.1016\/j.neucom.2018.08.059_bib0015","series-title":"in: Proceedings of the IEEE Transactions on Circuits and Systems for Video Technology","article-title":"Deep metric learning for crowdedness regression","author":"Wang","year":"2017"},{"issue":"2","key":"10.1016\/j.neucom.2018.08.059_bib0016","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/TPAMI.2004.1262185","article-title":"Spectral grouping using the nystr\u00f6m method","volume":"26","author":"Fowlkes","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2018.08.059_bib0017","series-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining","first-page":"715","article-title":"Spectral ensemble clustering","author":"Liu","year":"2015"},{"issue":"2","key":"10.1016\/j.neucom.2018.08.059_bib0018","first-page":"1","article-title":"Survey of clustering: algorithms and applications","volume":"3","author":"Greenlaw","year":"2013","journal-title":"Int. J. Inf. Retr. Res."},{"key":"10.1016\/j.neucom.2018.08.059_bib0019","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4292","article-title":"Quantifying and detecting collective motion by manifold learning","author":"Wang","year":"2017"},{"issue":"6","key":"10.1016\/j.neucom.2018.08.059_bib0020","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TNNLS.2015.2477537","article-title":"Salient band selection for hyperspectral image classification via manifold ranking","volume":"27","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2018.08.059_bib0021","series-title":"Proceedings of the Pattern Recognition","first-page":"701","article-title":"Learning must-link constraints for video segmentation based on spectral clustering","author":"Khoreva","year":"2014"},{"issue":"8","key":"10.1016\/j.neucom.2018.08.059_bib0022","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2018.08.059_bib0023","series-title":"Proceedings of the International Conference on Algorithmic Learning Theory","first-page":"367","article-title":"Fast spectral clustering via the nystr\u00f6m method","author":"Choromanska","year":"2013"},{"key":"10.1016\/j.neucom.2018.08.059_bib0024","series-title":"Proceedings of the Artificial Intelligence and Statistics","first-page":"304","article-title":"Sampling techniques for the nystrom method","author":"Kumar","year":"2009"},{"key":"10.1016\/j.neucom.2018.08.059_bib0025","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence","first-page":"1486","article-title":"Large-scale spectral clustering on graphs","author":"Liu","year":"2013"},{"key":"10.1016\/j.neucom.2018.08.059_bib0026","series-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining","first-page":"907","article-title":"Fast approximate spectral clustering","author":"Yan","year":"2009"},{"key":"10.1016\/j.neucom.2018.08.059_bib0027","article-title":"A review of nonnegative matrix factorization methods for clustering","author":"T\u00fcrkmen","year":"2015","journal-title":"CoRR"},{"issue":"9","key":"10.1016\/j.neucom.2018.08.059_bib0028","first-page":"2129","article-title":"Large-cone nonnegative matrix factorization","volume":"28","author":"Liu","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2018.08.059_bib0029","series-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"2492","article-title":"Fast spectral clustering with efficient large graph construction","author":"Zhu","year":"2017"},{"issue":"1","key":"10.1016\/j.neucom.2018.08.059_bib0030","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TPAMI.2014.2343221","article-title":"Convex discriminative multitask clustering","volume":"37","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.neucom.2018.08.059_bib0031","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TPAMI.2016.2544314","article-title":"Algorithm-dependent generalization bounds for multi-task learning","volume":"39","author":"Liu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2018.08.059_bib0032","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence","first-page":"3643","article-title":"Multi-task model and feature joint learning","author":"Li","year":"2015"},{"key":"10.1016\/j.neucom.2018.08.059_bib0033","series-title":"Proceedings of the IEEE International Conference on Data Mining","first-page":"159","article-title":"Learning the shared subspace for multi-task clustering and transductive transfer classification","author":"Gu","year":"2009"},{"issue":"5","key":"10.1016\/j.neucom.2018.08.059_bib0034","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1109\/TCYB.2014.2344015","article-title":"Multitask spectral clustering by exploring intertask correlation","volume":"45","author":"Yang","year":"2015","journal-title":"IEEE Trans. Cybernetics"},{"key":"10.1016\/j.neucom.2018.08.059_sbref0035","article-title":"Spectral theory of unsigned and signed graphs. applications to graph clustering: a survey","author":"Gallier","year":"2016","journal-title":"CoRR"},{"key":"10.1016\/j.neucom.2018.08.059_bib0036","series-title":"Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases","first-page":"451","article-title":"Improved minmax cut graph clustering with nonnegative relaxation","author":"Nie","year":"2010"},{"issue":"11","key":"10.1016\/j.neucom.2018.08.059_bib0037","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1109\/TCYB.2016.2585355","article-title":"Graph regularized non-negative low-rank matrix factorization for image clustering","volume":"47","author":"Li","year":"2017","journal-title":"IEEE Trans. Cybernetics"},{"key":"10.1016\/j.neucom.2018.08.059_bib0038","series-title":"Proceedings of the IEEE","first-page":"2278","article-title":"Gradient-based learning applied to document recognition","author":"LeCun","year":"1998"},{"key":"10.1016\/j.neucom.2018.08.059_bib0039","series-title":"Proceedings of the Machine Learning","first-page":"105","article-title":"Probabilistic dyadic data analysis with local and global consistency","author":"Cai","year":"2009"},{"key":"10.1016\/j.neucom.2018.08.059_bib0040","series-title":"Information and Knowledge Management","first-page":"911","article-title":"Modeling hidden topics on document manifold","author":"Cai","year":"2008"},{"issue":"3","key":"10.1016\/j.neucom.2018.08.059_bib0041","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1145\/183422.183423","article-title":"Automated learning of decision rules for text categorization","volume":"12","author":"Apt\u00e9","year":"1994","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.neucom.2018.08.059_bib0042","series-title":"Proceedings of the ACM Conference on Conference on Information and Knowledge Management (CIKM\u201907)","first-page":"741","article-title":"Regularized locality preserving indexing via spectral regression","author":"Cai","year":"2007"},{"issue":"12","key":"10.1016\/j.neucom.2018.08.059_bib0043","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TKDE.2005.198","article-title":"Document clustering using locality preserving indexing","volume":"17","author":"Cai","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"19","key":"10.1016\/j.neucom.2018.08.059_bib0044","doi-asserted-by":"crossref","first-page":"20149","DOI":"10.1007\/s11042-017-4566-4","article-title":"Improved spectral clustering based on nystr\u00f6m method","volume":"76","author":"Zhan","year":"2017","journal-title":"Multimedia Tools Appl."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523121831021X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092523121831021X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T12:42:00Z","timestamp":1656852120000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092523121831021X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":44,"alternative-id":["S092523121831021X"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2018.08.059","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2018,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spectral clustering based on iterative optimization for large-scale and high-dimensional data","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2018.08.059","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}