{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:29Z","timestamp":1740122789862,"version":"3.37.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012554","name":"Hubei Provincial Department of Education","doi-asserted-by":"publisher","award":["B2021099"],"award-info":[{"award-number":["B2021099"]}],"id":[{"id":"10.13039\/100012554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11262-9","type":"journal-article","created":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T07:02:27Z","timestamp":1679814147000},"page":"7321-7335","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Error Graph Regularized Nonnegative Matrix Factorization for Data Representation"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4057-9913","authenticated-orcid":false,"given":"Qiang","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Meijun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Junping","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"11262_CR1","doi-asserted-by":"publisher","DOI":"10.1017\/9781108779302","volume-title":"High-dimensional data analysis with low-dimensional models: principles, computation, and applications","author":"J Wright","year":"2022","unstructured":"Wright J, Ma Y (2022) High-dimensional data analysis with low-dimensional models: principles, computation, and applications. Cambridge University Press, Cambridge"},{"issue":"1","key":"11262_CR2","first-page":"629","volume":"18","author":"F Bach","year":"2017","unstructured":"Bach F (2017) Breaking the curse of dimensionality with convex neural networks. J Mach Learn Res 18(1):629\u2013681","journal-title":"J Mach Learn Res"},{"key":"11262_CR3","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.inffus.2020.01.005","volume":"59","author":"S Ayesha","year":"2020","unstructured":"Ayesha S, Hanif MK, Talib R (2020) Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf Fus 59:44\u201358","journal-title":"Inf Fus"},{"issue":"1","key":"11262_CR4","first-page":"2859","volume":"16","author":"JP Cunningham","year":"2015","unstructured":"Cunningham JP, Ghahramani Z (2015) Linear dimensionality reduction: survey, insights, and generalizations. J Mach Learn Res 16(1):2859\u20132900","journal-title":"J Mach Learn Res"},{"key":"11262_CR5","doi-asserted-by":"publisher","first-page":"107508","DOI":"10.1016\/j.patcog.2020.107508","volume":"107","author":"S Tasoulis","year":"2020","unstructured":"Tasoulis S, Pavlidis NG, Roos T (2020) Nonlinear dimensionality reduction for clustering. Pattern Recognit 107:107508","journal-title":"Pattern Recognit"},{"issue":"6755","key":"11262_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791","journal-title":"Nature"},{"issue":"2","key":"11262_CR7","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/MSP.2018.2877582","volume":"36","author":"X Fu","year":"2019","unstructured":"Fu X, Huang K, Sidiropoulos ND, Ma W-K (2019) Nonnegative matrix factorization for signal and data analytics: identifiability, algorithms, and applications. IEEE Signal Process Mag 36(2):59\u201380","journal-title":"IEEE Signal Process Mag"},{"key":"11262_CR8","doi-asserted-by":"crossref","unstructured":"Luo M, Nie F, Chang X, Yang Y, Hauptmann AG, Zheng Q (2017) Probabilistic non-negative matrix factorization and its robust extensions for topic modeling. In: AAAI conference on artificial intelligence 2017, pp 2308\u20132314","DOI":"10.1609\/aaai.v31i1.10832"},{"key":"11262_CR9","doi-asserted-by":"crossref","unstructured":"Shi T, Kang K, Choo J, Reddy CK (2018) Short-text topic modeling via non-negative matrix factorization enriched with local word-context correlations. In: Proceedings of the 2018 world wide web conference, pp 1105\u20131114","DOI":"10.1145\/3178876.3186009"},{"key":"11262_CR10","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.physa.2017.12.092","volume":"496","author":"X Ma","year":"2018","unstructured":"Ma X, Sun P, Wang Y (2018) Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks. Physica A-Stat Mech Appl 496:121\u2013136","journal-title":"Physica A-Stat Mech Appl"},{"issue":"2","key":"11262_CR11","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/TKDE.2018.2832205","volume":"31","author":"X Ma","year":"2019","unstructured":"Ma X, Dong D, Wang Q (2019) Community detection in multi-layer networks using joint nonnegative matrix factorization. IEEE Trans Knowl Data Eng 31(2):273\u2013286","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"11262_CR12","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TNSE.2020.3040407","volume":"8","author":"X Luo","year":"2020","unstructured":"Luo X, Liu Z, Shang M, Lou J, Zhou M (2020) Highly-accurate community detection via pointwise mutual information-incorporated symmetric non-negative matrix factorization. IEEE Trans Netw Sci Eng 8(1):463\u2013476","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"11262_CR13","doi-asserted-by":"crossref","unstructured":"Ailem M, Salah A, Nadif M (2017) Non-negative matrix factorization meets word embedding. In: Proceedings of the 40th international ACM Sigir conference on research and development in information retrieval, pp 1081\u20131084","DOI":"10.1145\/3077136.3080727"},{"issue":"10","key":"11262_CR14","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.tig.2018.07.003","volume":"34","author":"GL Stein-O\u2019Brien","year":"2018","unstructured":"Stein-O\u2019Brien GL, Arora R, Culhane AC, Favorov AV, Garmire LX, Greene CS, Goff LA, Li Y, Ngom A, Ochs MF, Xu Y, Fertig EJ (2018) Enter the matrix: factorization uncovers knowledge from omics. Trends Genet 34(10):790\u2013805","journal-title":"Trends Genet"},{"issue":"2","key":"11262_CR15","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/bioinformatics\/btx545","volume":"34","author":"Q Xiao","year":"2018","unstructured":"Xiao Q, Luo J, Liang C, Cai J, Ding P (2018) A graph regularized non-negative matrix factorization method for identifying microrna-disease associations. Bioinformatics 34(2):239\u2013248","journal-title":"Bioinformatics"},{"issue":"8","key":"11262_CR16","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","volume":"33","author":"D Cai","year":"2011","unstructured":"Cai D, He X, Han J, Huang TS (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548\u20131560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11262_CR17","doi-asserted-by":"publisher","first-page":"100423","DOI":"10.1016\/j.cosrev.2021.100423","volume":"42","author":"P De Handschutter","year":"2021","unstructured":"De Handschutter P, Gillis N, Siebert X (2021) A survey on deep matrix factorizations. Comput Sci Rev 42:100423","journal-title":"Comput Sci Rev"},{"issue":"10","key":"11262_CR18","doi-asserted-by":"publisher","first-page":"2840","DOI":"10.1016\/j.patcog.2013.03.007","volume":"46","author":"JJ-Y Wang","year":"2013","unstructured":"Wang JJ-Y, Bensmail H, Gao X (2013) Multiple graph regularized nonnegative matrix factorization. Pattern Recogn 46(10):2840\u20132847","journal-title":"Pattern Recogn"},{"key":"11262_CR19","unstructured":"Nie F, Li J, Li X (2016) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: IJCAI\u201916 proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 1881\u20131887"},{"key":"11262_CR20","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.knosys.2017.05.029","volume":"131","author":"Z Shu","year":"2017","unstructured":"Shu Z, Wu X, Fan H, Huang P, Wu D, Hu C, Ye F (2017) Parameter-less auto-weighted multiple graph regularized nonnegative matrix factorization for data representation. Knowl Based Syst 131:105\u2013112","journal-title":"Knowl Based Syst"},{"key":"11262_CR21","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.neucom.2019.11.070","volume":"382","author":"S Huang","year":"2020","unstructured":"Huang S, Xu Z, Kang Z, Ren Y (2020) Regularized nonnegative matrix factorization with adaptive local structure learning. Neurocomputing 382:196\u2013209","journal-title":"Neurocomputing"},{"key":"11262_CR22","unstructured":"Lee DD, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems 13, vol 13, pp 556\u2013562"},{"key":"11262_CR23","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jvcir.2017.08.002","volume":"49","author":"J Wu","year":"2017","unstructured":"Wu J, Feng L, Liu S, Sun M (2017) Image retrieval framework based on texton uniform descriptor and modified manifold ranking. J Vis Commun Image Represent 49:78\u201388","journal-title":"J Vis Commun Image Represent"},{"issue":"3","key":"11262_CR24","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TCSI.2019.2959886","volume":"67","author":"H Zhao","year":"2020","unstructured":"Zhao H, Zheng J, Deng W, Song Y (2020) Semi-supervised broad learning system based on manifold regularization and broad network. IEEE Trans Circuits Syst I Regul Pap 67(3):983\u2013994","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"issue":"9","key":"11262_CR25","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11262_CR26","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems 14, vol 14, pp 585\u2013591"},{"issue":"6","key":"11262_CR27","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1093\/aje\/kwz006","volume":"188","author":"L Schiffer","year":"2019","unstructured":"Schiffer L, Azhar R, Shepherd L, Ramos M, Geistlinger L, Huttenhower C, Dowd JB, Segata N, Waldron L (2019) Hmp16sdata: efficient access to the human microbiome project through bioconductor. Am J Epidemiol 188(6):1023\u20131026","journal-title":"Am J Epidemiol"},{"key":"11262_CR28","doi-asserted-by":"crossref","unstructured":"Xu W, Liu X, Gong, Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval, pp 267\u2013273","DOI":"10.1145\/860435.860485"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11262-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11063-023-11262-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11262-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T19:05:50Z","timestamp":1698519950000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/10.1007\/s11063-023-11262-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,26]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11262"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1007\/s11063-023-11262-9","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2023,3,26]]},"assertion":[{"value":"13 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}