{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:48Z","timestamp":1740108048977,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05462-w","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T05:06:34Z","timestamp":1605157594000},"page":"6851-6863","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Flexible data representation with graph convolution for semi-supervised learning"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-9680","authenticated-orcid":false,"given":"Fadi","family":"Dornaika","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"5462_CR1","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399\u20132434","journal-title":"J Mach Learn Res"},{"key":"5462_CR2","first-page":"585","volume":"14","author":"M Belkin","year":"2001","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inform Process Syst 14:585\u2013591","journal-title":"Adv Neural Inform Process Syst"},{"key":"5462_CR3","doi-asserted-by":"crossref","unstructured":"Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: IEEE international conference on computer vision, pp 1\u20137","DOI":"10.1109\/ICCV.2007.4408856"},{"issue":"1","key":"5462_CR4","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/TCYB.2015.2399456","volume":"46","author":"F Dornaika","year":"2016","unstructured":"Dornaika F, El Traboulsi Y (2016) Learning flexible graph-based semi-supervised embedding. IEEE Trans Cybern 46(1):206\u2013218","journal-title":"IEEE Trans Cybern"},{"key":"5462_CR5","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.patcog.2016.07.029","volume":"61","author":"F Dornaika","year":"2017","unstructured":"Dornaika F, Traboulsi YE (2017) Matrix exponential based semi-supervised discriminant embedding. Pattern Recognit 61:92\u2013103","journal-title":"Pattern Recognit"},{"key":"5462_CR6","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2019.03.002","volume":"114","author":"F Dornaika","year":"2019","unstructured":"Dornaika F, Traboulsi YE (2019) Joint sparse graph and flexible embedding for graph-based semi-supervised learning. Neural Netw 114:91\u201395","journal-title":"Neural Netw"},{"key":"5462_CR7","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2017.08.002","volume":"95","author":"F Dornaika","year":"2017","unstructured":"Dornaika F, Kejani M, Bosaghzadeh A (2017) Graph construction using adaptive local hybrid coding scheme. Neural Netw 95:91\u2013101","journal-title":"Neural Netw"},{"key":"5462_CR8","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.neucom.2015.04.042","volume":"167","author":"Y El Traboulsi","year":"2015","unstructured":"El Traboulsi Y, Dornaika F, Assoum A (2015) Kernel flexible manifold embedding for pattern classification. Neurocomputing 167:517\u2013527","journal-title":"Neurocomputing"},{"key":"5462_CR9","unstructured":"Fang X, Xu Y, Li X, Lai Z, Wong WK (2015) Learning a nonnegative sparse graph for linear regression"},{"key":"5462_CR10","unstructured":"Franceschi L, Niepert M, Pontil M, He X (2019) Learning discrete structures for graph neural networks. In: International conference on machine learning, pp 1972\u20131982"},{"key":"5462_CR11","unstructured":"Hamilton ZYW, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024\u20131034"},{"issue":"6","key":"5462_CR12","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1109\/TCYB.2013.2272642","volume":"44","author":"C Hou","year":"2014","unstructured":"Hou C, Nie F, Li X, Yi D, Wu Y (2014) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793\u2013804","journal-title":"IEEE Trans Cybern"},{"key":"5462_CR13","doi-asserted-by":"publisher","first-page":"377","DOI":"10.5194\/isprsannals-I-3-377-2012","volume":"3","author":"H Huang","year":"2012","unstructured":"Huang H, Liu J, Pan Y (2012) Semi-supervised marginal fisher analysis for hyperspectral image classification. ISPRS Ann Photogramm Remote Sens Spatial Inform Sci 3:377\u2013382","journal-title":"ISPRS Ann Photogramm Remote Sens Spatial Inform Sci"},{"key":"5462_CR14","doi-asserted-by":"crossref","unstructured":"Kar P, Li S, Narasimhan H, Chawla S, Sebastiani F (2016)Online optimization methods for the quantification problem. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2939672.2939832"},{"key":"5462_CR15","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.neunet.2020.04.016","volume":"127","author":"MT Kejani","year":"2020","unstructured":"Kejani MT, Dornaika F, Talebi H (2020) Graph convolution networks with manifold regularization for semi-supervised learning. Neural Netw 127:160\u2013167","journal-title":"Neural Netw"},{"key":"5462_CR16","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations"},{"key":"5462_CR17","unstructured":"Klicpera ABJ, Gunnemann S (2019) Predict then propagate: graph neural networks meet personalized pagerank. In: International conference on learning representations"},{"key":"5462_CR18","unstructured":"Korda N, Szorenyi, B, Li S (2016) Distributed clustering of linear bandits in peer to peer networks. In: International conference on machine learning"},{"key":"5462_CR19","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"5462_CR20","doi-asserted-by":"crossref","unstructured":"Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE conference on computer vision and pattern recognition, pp 2169\u20132178","DOI":"10.1109\/CVPR.2006.68"},{"issue":"11","key":"5462_CR21","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"5462_CR22","doi-asserted-by":"crossref","unstructured":"Li LJ, Li FF (2007) What, where and who? Classifying events by scene and object recognition. In: IEEE international conference on computer vision, pp 1\u20138","DOI":"10.1109\/ICCV.2007.4408872"},{"key":"5462_CR23","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neucom.2018.05.122","volume":"348","author":"Y Liang","year":"2018","unstructured":"Liang Y, You L, Lu X, He Z, Wang H (2018) Low-rank projection learning via graph embedding. Neurocomputing 348:97\u2013106","journal-title":"Neurocomputing"},{"key":"5462_CR24","doi-asserted-by":"crossref","unstructured":"Li S, Chen W, Li S, Leung K-S (2019) Improved algorithm on online clustering of bandits. arXiv:1902.09162","DOI":"10.24963\/ijcai.2019\/405"},{"key":"5462_CR25","doi-asserted-by":"crossref","unstructured":"Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. arXiv:1502.03473","DOI":"10.1145\/2911451.2911548"},{"issue":"9","key":"5462_CR26","doi-asserted-by":"publisher","first-page":"2624","DOI":"10.1109\/JPROC.2012.2197809","volume":"100","author":"W Liu","year":"2012","unstructured":"Liu W, Wang J, Chang S-F (2012) Graph-based semi-supervised learning. Proc IEEE 100(9):2624\u20132638","journal-title":"Proc IEEE"},{"key":"5462_CR27","doi-asserted-by":"crossref","unstructured":"Liu W, Chang S-F (2009) Robust multi-class transductive learning with graphs. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 381\u2013388. IEEE","DOI":"10.1109\/CVPR.2009.5206871"},{"key":"5462_CR28","doi-asserted-by":"crossref","unstructured":"Mahadik K, Wu Q, Li S, Sabne A (2020) Fast distributed bandits for online recommendation systems","DOI":"10.1145\/3392717.3392748"},{"key":"5462_CR29","unstructured":"Nene SA, Nayar S, Murase H (1996) Columbia object image library (COIL-20). In: Technical report CUCS-005-96"},{"issue":"7","key":"5462_CR30","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TIP.2010.2044958","volume":"19","author":"F Nie","year":"2010","unstructured":"Nie F, Xu D, Tsang IW-H, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans Image Process 19(7):1921\u20131932","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"5462_CR31","doi-asserted-by":"publisher","first-page":"3682","DOI":"10.1109\/TCYB.2019.2910751","volume":"50","author":"F Nie","year":"2020","unstructured":"Nie F, Wang Z, Wang R, Li X (2020) Submanifold-preserving discriminant analysis with an auto-optimized graph. IEEE Trans Cybern 50(8):3682\u20133695","journal-title":"IEEE Trans Cybern"},{"key":"5462_CR32","doi-asserted-by":"crossref","unstructured":"Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Thirty-first AAAI conference on artificial intelligence, pp 1501\u20131511","DOI":"10.1109\/TIP.2017.2754939"},{"key":"5462_CR33","doi-asserted-by":"crossref","unstructured":"Nie F, Dong F, Li X (2020) Unsupervised and semisupervised projection with graph optimization. In: IEEE transactions on neural networks and learning systems","DOI":"10.1109\/TNNLS.2020.2984958"},{"issue":"1","key":"5462_CR34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T Ojala","year":"1996","unstructured":"Ojala T, Pietik\u00e4inen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51\u201359","journal-title":"Pattern Recognit"},{"issue":"5","key":"5462_CR35","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.patrec.2009.11.005","volume":"31","author":"L Qiao","year":"2010","unstructured":"Qiao L, Chen S, Tan X (2010) Sparsity preserving discriminant analysis for single training image face recognition. Pattern Recognit Lett 31(5):422\u2013429","journal-title":"Pattern Recognit Lett"},{"key":"5462_CR36","unstructured":"Qu M, Bengio Y, Tang J (2019) GMNN: graph Markov neural networks. In: International conference on machine learning, pp 5241\u20135250"},{"issue":"6","key":"5462_CR37","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1016\/j.patcog.2011.12.006","volume":"45","author":"B Raducanu","year":"2012","unstructured":"Raducanu B, Dornaika F (2012) A supervised non-linear dimensionality reduction approach for manifold learning. Pattern Recognit 45(6):2432\u20132444","journal-title":"Pattern Recognit"},{"issue":"5500","key":"5462_CR38","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"5500","key":"5462_CR39","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319\u20132323","journal-title":"Science"},{"key":"5462_CR40","unstructured":"Velickovic WL H P L YB P, Fedus W, Hjelm RD (2019) Deep graph infomax. In: International conference on learning representations"},{"key":"5462_CR41","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.ins.2014.02.145","volume":"274","author":"M Wan","year":"2014","unstructured":"Wan M, Li M, Yang G, Gai S, Jin Z (2014) Feature extraction using two-dimensional maximum embedding difference. Inf Sci 274:55\u201369","journal-title":"Inf Sci"},{"key":"5462_CR42","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.fss.2016.06.001","volume":"318","author":"M Wan","year":"2017","unstructured":"Wan M, Lai Z, Yang G, Yang Z, Zhang F, Zheng H (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120\u2013131","journal-title":"Fuzzy Sets Syst"},{"issue":"11","key":"5462_CR43","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.1016\/j.patcog.2009.04.015","volume":"42","author":"F Wang","year":"2009","unstructured":"Wang F, Wang X, Zhang D, Zhang C, Li T (2009) Marginface: a novel face recognition method by average neighborhood margin maximization. Pattern Recognit 42(11):2863\u20132875","journal-title":"Pattern Recognit"},{"key":"5462_CR44","unstructured":"Wang H, Leskovec J (2020) Unifying graph convolutional neural networks and label propagation. arXiv preprint arXiv:2002.06755"},{"issue":"4","key":"5462_CR45","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TCYB.2018.2884715","volume":"50","author":"J Wen","year":"2020","unstructured":"Wen J, Xu Y, Liu H (2020) Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans Cybern 50(4):1418\u20131429","journal-title":"IEEE Trans Cybern"},{"key":"5462_CR46","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.patcog.2017.09.003","volume":"74","author":"H Wu","year":"2018","unstructured":"Wu H, Prasad S (2018) Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels. Pattern Recognit 74:212\u2013224","journal-title":"Pattern Recognit"},{"key":"5462_CR47","unstructured":"Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, pp 6861\u20136871"},{"issue":"1","key":"5462_CR48","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, Zhang H, Yang Q, Lin S (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":"3","key":"5462_CR49","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1016\/j.patcog.2011.08.024","volume":"45","author":"G Yu","year":"2012","unstructured":"Yu G, Zhang G, Domeniconi C, Yu Z, You J (2012) Semi-supervised classification based on random subspace dimensionality reduction. Pattern Recognit 45(3):1119\u20131135","journal-title":"Pattern Recognit"},{"issue":"10","key":"5462_CR50","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2014.2359471","volume":"26","author":"Y Yuan","year":"2015","unstructured":"Yuan Y, Mou L, Lu X (2015) Scene recognition by manifold regularized deep learning architecture. IEEE Trans Neural Netw Learn Syst 26(10):2222\u20132233","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5462_CR51","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neunet.2018.07.017","volume":"108","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Jia L, Zhao M, Ye Q, Zhang M, Wang M (2018) Adaptive non-negative projective semi-supervised learning for inductive classification. Neural Netw 108:128\u2013145","journal-title":"Neural Netw"},{"key":"5462_CR52","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.neucom.2019.08.036","volume":"370","author":"M Zhao","year":"2019","unstructured":"Zhao M, Zhang Y, Zhang Z, Liu J, Kong W (2019) Alg: Adaptive low-rank graph regularization for scalable semi-supervised and unsupervised learning. Neurocomputing 370:16\u201327","journal-title":"Neurocomputing"},{"key":"5462_CR53","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.patcog.2019.05.004","volume":"93","author":"R Zhu","year":"2019","unstructured":"Zhu R, Dornaika F, Ruichek Y (2019) Joint graph based embedding and feature weighting for image classification. Pattern Recognit 93:458\u2013469","journal-title":"Pattern Recognit"},{"key":"5462_CR54","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neunet.2018.12.008","volume":"111","author":"R Zhu","year":"2019","unstructured":"Zhu R, Dornaika F, Ruichek Y (2019) Learning a discriminant graph-based embedding with feature selection for image categorization. Neural Netw 111:35\u201346","journal-title":"Neural Netw"},{"key":"5462_CR55","doi-asserted-by":"publisher","first-page":"107425","DOI":"10.1016\/j.patcog.2020.107425","volume":"107","author":"R Zhu","year":"2020","unstructured":"Zhu R, Dornaika F, Ruichek Y (2020) Semi-supervised elastic manifold embedding with deep learning architecture. Pattern Recognit 107:107425","journal-title":"Pattern Recognit"},{"key":"5462_CR56","unstructured":"Zhu X, Ghahramani Z, Lafferty J et al (2003) Semi-supervised learning using Gaussian fields and harmonic functions, vol\u00a03, pp 912\u2013919"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05462-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05462-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05462-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T19:33:48Z","timestamp":1622403228000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05462-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,12]]},"references-count":56,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5462"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05462-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,11,12]]},"assertion":[{"value":"24 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2020","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 the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}