{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:49:25Z","timestamp":1762325365842,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603159, 62162033","61902160"],"award-info":[{"award-number":["61603159, 62162033","61902160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2008085QF305"],"award-info":[{"award-number":["2008085QF305"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-03881-x","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T05:02:45Z","timestamp":1659416565000},"page":"7713-7727","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dual local learning regularized NMF with sparse and orthogonal constraints"],"prefix":"10.1007","volume":"53","author":[{"given":"Zhenqiu","family":"Shu","sequence":"first","affiliation":[]},{"given":"Furong","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"Wenli","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Congzhe","family":"You","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"3881_CR1","doi-asserted-by":"crossref","unstructured":"Shu Z, Weng Z, Yu Z et al (2022) Correntropy-based dual graph regularized nonnegative matrix factorization with Lp smoothness for data representation. Appl Intell 52(7):7653\u20137669","DOI":"10.1007\/s10489-021-02826-0"},{"issue":"5","key":"3881_CR2","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1109\/TKDE.2019.2893956","volume":"32","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Zhang Y, Liu G et al (2020) Joint label prediction based semi-supervised adaptive concept factorization for robust data representation. IEEE Trans Knowl Data Eng 32(5):952\u2013970","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3881_CR3","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 et al (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":"3881_CR4","first-page":"41","volume-title":"Principal component analysis","author":"I Jolliffe","year":"1989","unstructured":"Jolliffe I (1989) Principal component analysis. Springer-Verlag, New York, NY, USA, pp 41\u201364"},{"issue":"7","key":"3881_CR5","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/34.598228","volume":"19","author":"P Belhumeur","year":"1997","unstructured":"Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Patt Anal Mach Intell 19(7):711\u2013720","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"issue":"6755","key":"3881_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"2000","unstructured":"Lee DD, Seung HS (2000) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791","journal-title":"Nature"},{"issue":"1","key":"3881_CR7","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","volume":"29","author":"S Yan","year":"2007","unstructured":"Yan S, Xu D, Zhang B (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40\u201351","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"3881_CR8","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.ins.2020.05.073","volume":"536","author":"H Cai","year":"2020","unstructured":"Cai H, Liu B, Xiao Y (2020) Semi-supervised multi-view clustering based on orthonormality-constrained nonnegative matrix factorization. Inf Sci 536(10):171\u2013184","journal-title":"Inf Sci"},{"key":"3881_CR9","doi-asserted-by":"publisher","unstructured":"Zhang D, Wu X-J (2020) Scalable discrete matrix factorization and semantic autoencoder for cross-media retrieval. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2020.3032017","DOI":"10.1109\/TCYB.2020.3032017"},{"key":"3881_CR10","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1109\/JBHI.2020.2975199","volume":"99","author":"C Jiao","year":"2020","unstructured":"Jiao C, Gao Y, Yu N et al (2020) Hyper-graph regularized constrained NMF for selecting differentially expressed genes and tumor classification. IEEE J Biomed Health Inform 99:3002\u20133011","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"3881_CR11","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1109\/TGRS.2019.2946751","volume":"58","author":"X Lu","year":"2020","unstructured":"Lu X, Dong L, Yuan Y (2020) Subspace clustering constrained sparse NMF for hyperspectral unmixing. IEEE Trans Geosci Remote Sens 58(5):3007\u20133019. https:\/\/doi.org\/10.1109\/TGRS.2019.2946751","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3881_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3067218","volume":"70","author":"X Xiu","year":"2021","unstructured":"Xiu X, Fan J, Yang Y et al (2021) Fault detection using structured joint sparse nonnegative matrix factorization. IEEE Trans Instrum Meas 70:1\u201311","journal-title":"IEEE Trans Instrum Meas"},{"issue":"8","key":"3881_CR13","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 et al (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":"3881_CR14","unstructured":"Gu Q, Zhou J (2009) Local learning regularized nonnegative matrix factorization. Twenty-first Int Joint Conf Artif Intell:1044\u20131051"},{"issue":"6","key":"3881_CR15","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1016\/j.patcog.2011.12.015","volume":"45","author":"F Shang","year":"2012","unstructured":"Shang F, Jiao LC, Wang F (2012) Graph dual regularization non-negative matrix factorization for co-clustering. Pattern Recogn 45(6):2237\u20132250","journal-title":"Pattern Recogn"},{"issue":"10","key":"3881_CR16","first-page":"3002","volume":"24","author":"C Wang","year":"2019","unstructured":"Wang C, Yu N, Wu M et al (2019) Dual hyper-graph regularized supervised NMF for selecting differentially expressed genes and tumor classification. IEEE Trans Comput Biol Bioinform 24(10):3002\u20133011","journal-title":"IEEE Trans Comput Biol Bioinform"},{"key":"3881_CR17","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ins.2020.04.017","volume":"528","author":"Z Shu","year":"2020","unstructured":"Shu Z, Wu X, You C et al (2020) Rank-constrained nonnegative matrix factorization for data representation. Inf Sci 528:133\u2013146","journal-title":"Inf Sci"},{"issue":"29","key":"3881_CR18","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2015.10.048","volume":"198","author":"Z Shu","year":"2016","unstructured":"Shu Z, Zhou J, Huang P et al (2016) Local and global regularized sparse coding for data representation. Neurocomputing 198(29):188\u2013197","journal-title":"Neurocomputing"},{"key":"3881_CR19","doi-asserted-by":"publisher","unstructured":"Shu Z, Sun Y, Tang J et al (2022) Adaptive graph regularized deep semi-nonnegative matrix factorization for data representation. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-022-10882-x","DOI":"10.1007\/s11063-022-10882-x"},{"issue":"7","key":"3881_CR20","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TPAMI.2011.217","volume":"34","author":"H Liu","year":"2012","unstructured":"Liu H, Wu Z, Cai D, Huang TS (2012) Constrained nonnegative matrix factorization for image representation. IEEE Trans Pattern Anal Mach Intell 34(7):1299\u20131311","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"3881_CR21","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1109\/TNNLS.2017.2691725","volume":"29","author":"Z Li","year":"2018","unstructured":"Li Z, Tang J (2018) Robust structured nonnegative matrix factorization for image representation. IEEE Trans Neural Networks Learn Syst 29(5):1947\u20131960","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"3","key":"3881_CR22","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1109\/TPAMI.2016.2554555","volume":"39","author":"G Trigeorgis","year":"2017","unstructured":"Trigeorgis G, Bousmalis K et al (2017) A deep matrix factorization method for learning attribute representations. IEEE Trans Pattern Anal Mach Intell 39(3):1692\u20131700","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"3881_CR23","doi-asserted-by":"publisher","first-page":"1392","DOI":"10.1109\/TCSVT.2016.2539779","volume":"27","author":"Y Lu","year":"2017","unstructured":"Lu Y, Lai Z, Xu Y, Li X et al (2017) Nonnegative discriminant matrix factorization. IEEE Trans Circ Syst Video Technol 27(7):1392\u20131405","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"3881_CR24","doi-asserted-by":"publisher","unstructured":"Ma J, Zhang Y, Zhang L (2021) Discriminative subspace matrix factorization for multi-view data clustering. Patt Recogn. https:\/\/doi.org\/10.1016\/j.patcog.2020.107676","DOI":"10.1016\/j.patcog.2020.107676"},{"key":"3881_CR25","doi-asserted-by":"publisher","first-page":"4267","DOI":"10.1109\/JSTARS.2021.3072044","volume":"14","author":"X Li","year":"2021","unstructured":"Li X, Zhang Y, Ge Z et al (2021) Adaptive nonnegative sparse representation for hyperspectral image super-resolution. IEEE J Selected Topics Appl Earth Observ Remote Sensing 14:4267\u20134283","journal-title":"IEEE J Selected Topics Appl Earth Observ Remote Sensing"},{"key":"3881_CR26","doi-asserted-by":"publisher","unstructured":"Chen J, Yang S, Wang Z et al (2021) Efficient sparse representation for learning with high-dimensional data. IEEE Trans Neural Networks Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3119278","DOI":"10.1109\/TNNLS.2021.3119278"},{"key":"3881_CR27","doi-asserted-by":"crossref","unstructured":"Ding C, Li T, Pen W et al (2006) Orthogonal nonnegative matrix tri-factorizations for clustering. ACM SIGKDD Int Conf Knowledge Discovery Data Mining:126\u2013135","DOI":"10.1145\/1150402.1150420"},{"key":"3881_CR28","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1016\/j.neucom.2015.07.068","volume":"171","author":"R Shang","year":"2016","unstructured":"Shang R, Zhang Z, Jiao L et al (2016) Self-representation based dual-graph regularized feature selection clustering. Neurocomputing 171:1242\u20131253","journal-title":"Neurocomputing"},{"key":"3881_CR29","doi-asserted-by":"crossref","unstructured":"Meng Y, Shang R, Jiao L, et al. Dual-graph regularized non-negative matrix factorization with sparse and orthogonal constraints Eng Appl Artif Intell, 2018, 69: 24\u201335","DOI":"10.1016\/j.engappai.2017.11.008"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03881-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03881-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03881-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T02:51:44Z","timestamp":1678935104000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03881-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":29,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["3881"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03881-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,8,2]]},"assertion":[{"value":"7 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}