{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T11:40:41Z","timestamp":1769168441179,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2019,3,22]],"date-time":"2019-03-22T00:00:00Z","timestamp":1553212800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant 61671480"],"award-info":[{"award-number":["Grant 61671480"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s12559-019-09637-z","type":"journal-article","created":{"date-parts":[[2019,3,22]],"date-time":"2019-03-22T13:03:36Z","timestamp":1553259816000},"page":"841-854","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Ensemble p-Laplacian Regularization for Scene Image Recognition"],"prefix":"10.1007","volume":"11","author":[{"given":"Xueqi","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5388-9080","authenticated-orcid":false,"given":"Weifeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dapeng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yicong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,22]]},"reference":[{"key":"9637_CR1","doi-asserted-by":"crossref","unstructured":"Subramanya, Bilmes J. Soft-supervised learning for text classification. EMNLP. 2008:1090\u20139.","DOI":"10.3115\/1613715.1613857"},{"issue":"1","key":"9637_CR2","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s12559-016-9439-z","volume":"9","author":"S Scardapane","year":"2017","unstructured":"Scardapane S, Uncini A. Semi-supervised echo state networks for audio classification. Cogn Comput. 2017;9(1):125\u201335.","journal-title":"Cogn Comput"},{"key":"9637_CR3","unstructured":"Zhou D, Bousquet O, Lal TN, et al. Learning with local and global consistency. Adv Neural Inf Proces Syst. 2004:321\u20138."},{"issue":"4","key":"9637_CR4","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1016\/j.patcog.2011.10.008","volume":"45","author":"M Zhao","year":"2012","unstructured":"Zhao M, Zhang Z, Chow TWS. Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction. Pattern Recogn. 2012;45(4):1482\u201399.","journal-title":"Pattern Recogn"},{"issue":"1","key":"9637_CR5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s12559-016-9433-5","volume":"9","author":"L Oneto","year":"2017","unstructured":"Oneto L, Bisio F, Cambria E, Anguita D. Semi-supervised learning for affective common-sense reasoning. Cogn Comput. 2017;9(1):18\u201342.","journal-title":"Cogn Comput"},{"issue":"1","key":"9637_CR6","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s12559-017-9489-x","volume":"10","author":"S Ding","year":"2018","unstructured":"Ding S, Xi X, Liu Z, Qiao H, Zhang B. A novel manifold regularized online semi-supervised learning model. Cogn Comput. 2018;10(1):49\u201361.","journal-title":"Cogn Comput"},{"key":"9637_CR7","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.knosys.2014.12.014","volume":"76","author":"M Zhao","year":"2015","unstructured":"Zhao M, Chow TWS, Zhang Z, Li B. Automatic image annotation via compact graph based semi-supervised learning. Knowl-Based Syst. 2015;76:148\u201365.","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"9637_CR8","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/s12559-016-9386-8","volume":"8","author":"FH Khan","year":"2016","unstructured":"Khan FH, Qamar U, Bashir S. Multi-objective model selection (MOMS)-based semi-supervised framework for sentiment analysis. Cogn Comput. 2016;8(4):614\u201328.","journal-title":"Cogn Comput"},{"issue":"Nov","key":"9637_CR9","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res. 2006;7(Nov):2399\u2013434.","journal-title":"J Mach Learn Res"},{"issue":"7","key":"9637_CR10","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TIP.2013.2255302","volume":"22","author":"W Liu","year":"2013","unstructured":"Liu W, Tao D. Multiview Hessian regularization for image annotation. IEEE Trans Image Process. 2013;22(7):2676\u201387.","journal-title":"IEEE Trans Image Process"},{"key":"9637_CR11","unstructured":"Liu X, Shi J, Wang C. Hessian regularization based non-negative matrix factorization for gene expression data clustering. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, IEEE, 2015: 4130\u20134133."},{"key":"9637_CR12","doi-asserted-by":"crossref","unstructured":"Zhu J, Shi J. Hessian regularization based semi-supervised dimensionality reduction for neuroimaging data of Alzheimer\u2019s disease. Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium, IEEE, 2014: 165\u2013168.","DOI":"10.1109\/ISBI.2014.6867835"},{"key":"9637_CR13","doi-asserted-by":"crossref","unstructured":"B\u00fchler T, Hein M. Spectral clustering based on the graph p-Laplacian. In: Proceedings of the 26th Annual International Conference on Machine Learning: ACM; 2009. p. 81\u20138.","DOI":"10.1145\/1553374.1553385"},{"key":"9637_CR14","unstructured":"Kim KI, Steinke F, Hein M. Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction. Adv Neural Inf Proces Syst. 2009:979\u201387."},{"issue":"3","key":"9637_CR15","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1215\/ijm\/1258138202","volume":"47","author":"H Takeuchi","year":"2003","unstructured":"Takeuchi H. The spectrum of the p-Laplacian and p-harmonic morphisms on graphs. Ill J Math. 2003;47(3):939\u201355.","journal-title":"Ill J Math"},{"issue":"7","key":"9637_CR16","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/S0362-546X(97)00530-0","volume":"32","author":"W Allegretto","year":"1998","unstructured":"Allegretto W, Xi HY. A Picone\u2019s identity for the p-Laplacian and applications. Nonlinear Anal: Theory, Methods Appl. 1998;32(7):819\u201330.","journal-title":"Nonlinear Anal: Theory, Methods Appl"},{"issue":"6","key":"9637_CR17","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TPAMI.2012.57","volume":"34","author":"B Geng","year":"2012","unstructured":"Geng B, Tao D, Xu C, et al. Ensemble manifold regularization. IEEE Trans Pattern Anal Mach Intell. 2012;34(6):1227\u201333.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"9637_CR18","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1109\/TCSVT.2009.2017400","volume":"19","author":"M Wang","year":"2009","unstructured":"Wang M, Hua XS, Hong R, et al. Unified video annotation via multigraph learning. IEEE Trans Circuits Syst Video Technol. 2009;19(5):733\u201346.","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"6","key":"9637_CR19","first-page":"3742","volume":"62","author":"C Hong","year":"2015","unstructured":"Hong C, Yu J, Tao D, Wang M. Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron. 2015;62(6):3742\u201351.","journal-title":"IEEE Trans Ind Electron"},{"issue":"4","key":"9637_CR20","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1109\/TCYB.2016.2535122","volume":"47","author":"J Zhang","year":"2017","unstructured":"Zhang J, Han Y, Tang J, Hu Q, Jiang J. Semi-supervised image-to-video adaptation for video action recognition. IEEE Trans Cybern. 2017;47(4):960\u201373.","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"9637_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12559-012-9147-2","volume":"5","author":"X Bian","year":"2013","unstructured":"Bian X, Zhang T, Zhang X, Yan LX, Li B. Clustering-based extraction of near border data samples for remote sensing image classification. Cogn Comput. 2013;5(1):19\u201331.","journal-title":"Cogn Comput"},{"key":"9637_CR22","doi-asserted-by":"crossref","unstructured":"Hu W, Cheung G, Li X, et al. Graph-based joint denoising and super-resolution of generalized piecewise smooth images. Image Processing (ICIP), 2014 IEEE International Conference. IEEE, 2014: 2056\u20132060.","DOI":"10.1109\/ICIP.2014.7025412"},{"issue":"2","key":"9637_CR23","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1109\/TIP.2012.2218825","volume":"22","author":"Y Luo","year":"2013","unstructured":"Luo Y, Tao D, Geng B, Xu C, Maybank SJ. Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process. 2013;22(2):523\u201336.","journal-title":"IEEE Trans Image Process"},{"key":"9637_CR24","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.sigpro.2015.09.026","volume":"124","author":"J Jiang","year":"2016","unstructured":"Jiang J, Hu R, Wang Z, Cai Z. CDMMA: coupled discriminant multi-manifold analysis for matching low-resolution face images. Signal Process. 2016;124:162\u201372.","journal-title":"Signal Process"},{"issue":"2","key":"9637_CR25","first-page":"1137","volume":"14","author":"R Kohavi","year":"1995","unstructured":"Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI. 1995;14(2):1137\u201345.","journal-title":"IJCAI"},{"issue":"1","key":"9637_CR26","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s10994-010-5201-z","volume":"81","author":"D Luo","year":"2010","unstructured":"Luo D, Huang H, Ding C, Nie F. On the eigenvectors of p-Laplacian. Mach Learn. 2010;81(1):37\u201351.","journal-title":"Mach Learn"},{"key":"9637_CR27","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S. Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, 2010: 270\u2013279.","DOI":"10.1145\/1869790.1869829"},{"key":"9637_CR28","first-page":"361","volume-title":"Regularization on discrete spaces. Joint Pattern Recognition Symposium","author":"D Zhou","year":"2005","unstructured":"Zhou D, Sch\u00f6lkopf B. Regularization on discrete spaces. Joint Pattern Recognition Symposium. Berlin, Heidelberg: Springer; 2005. p. 361\u20138."},{"issue":"8","key":"9637_CR29","first-page":"5120","volume":"63","author":"W Liu","year":"2016","unstructured":"Liu W, Zha ZJ, Wang Y, et al. p-Laplacian regularized sparse coding for human activity recognition. IEEE Trans Ind Electron. 2016;63(8):5120\u20139.","journal-title":"IEEE Trans Ind Electron"},{"issue":"8","key":"9637_CR30","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.1109\/TCYB.2018.2833843","volume":"49","author":"Weifeng Liu","year":"2019","unstructured":"Liu W, Ma X, Zhou Y, Tao D, and Cheng J.. p-Laplacian regularization for scene recognition. IEEE Transactions on Cybernetics, to be published, doi: \nhttps:\/\/doi.org\/10.1109\/TCYB.2018.2833843\n\n, 2018.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"9637_CR31","doi-asserted-by":"publisher","unstructured":"Ma X, Liu W, Li S, Tao D, and Zhou Y. Hypergraph p-Laplacian regularization for remotely sensed image recognition. IEEE Trans Geosci Remote Sens, to be published, doi: \nhttps:\/\/doi.org\/10.1109\/TGRS.2018.2867570\n\n, 2018.","DOI":"10.1109\/TGRS.2018.2867570"},{"key":"9637_CR32","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv. 2014:1409.1556."},{"issue":"2","key":"9637_CR33","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI, et al. The Pascal visual object classes (VOC) challenge. Int J Comput Vis. 2010;88(2):303\u201338.","journal-title":"Int J Comput Vis"},{"key":"9637_CR34","first-page":"2169","volume-title":"Beyond bags of features: spatial pyramid matching for recognizing natural scene categories","author":"S Lazebnik","year":"2006","unstructured":"Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. New York, NY, USA: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906); 2006. p. 2169\u201378."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-019-09637-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-019-09637-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-019-09637-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,21]],"date-time":"2020-03-21T00:27:57Z","timestamp":1584750477000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-019-09637-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,22]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["9637"],"URL":"https:\/\/doi.org\/10.1007\/s12559-019-09637-z","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,22]]},"assertion":[{"value":"28 March 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}