{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:12Z","timestamp":1740160272651,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2015,11,9]],"date-time":"2015-11-09T00:00:00Z","timestamp":1447027200000},"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":["61170158"],"award-info":[{"award-number":["61170158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2017,6]]},"DOI":"10.1007\/s13042-015-0458-y","type":"journal-article","created":{"date-parts":[[2015,11,9]],"date-time":"2015-11-09T00:50:07Z","timestamp":1447030207000},"page":"877-889","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph based semi-supervised learning via label fitting"],"prefix":"10.1007","volume":"8","author":[{"given":"Weiya","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,11,9]]},"reference":[{"key":"458_CR1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1613\/jair.1509","volume":"23","author":"NV Chawla","year":"2005","unstructured":"Chawla NV, Karakoulas GI (2005) Learning from labeled and unlabeled data: an empirical study across techniques and domains. J Artif Intell Res (JAIR) 23:331\u2013366","journal-title":"J Artif Intell Res (JAIR)"},{"key":"458_CR2","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/9780262033589.001.0001","volume-title":"Semi-supervised learning","author":"O Chapelle","year":"2006","unstructured":"Chapelle O, Sch\u00f6lkopf B, Zien A (eds) (2006) Semi-supervised learning. MIT Press, Cambridge, MA"},{"issue":"1","key":"458_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00196ED1V01Y200906AIM006","volume":"3","author":"X Zhu","year":"2009","unstructured":"Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3(1):1\u2013130","journal-title":"Synth Lect Artif Intell Mach Learn"},{"issue":"7","key":"458_CR4","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1109\/TIP.2010.2044958","volume":"19","author":"F Nie","year":"2010","unstructured":"Nie F, Xu D, Tsang IWH, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. Image Process IEEE Trans 19(7):1921\u20131932","journal-title":"Image Process IEEE Trans"},{"key":"458_CR5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neucom.2014.11.018","volume":"152","author":"Q Gao","year":"2015","unstructured":"Gao Q, Huang Y, Gao X, Shen W, Zhang H (2015) A novel semi-supervised learning for face recognition. Neurocomputing 152:69\u201376","journal-title":"Neurocomputing"},{"key":"458_CR6","doi-asserted-by":"crossref","unstructured":"Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: Computer Vision, 2007. ICCV 2007. IEEE 11th international conference on IEEE, pp 1\u20137","DOI":"10.1109\/ICCV.2007.4408856"},{"issue":"16","key":"458_CR7","first-page":"321","volume":"16","author":"D Zhou","year":"2004","unstructured":"Zhou D, Bousquet O, Lal TN, Weston J, Sch\u00f6lkopf B (2004) Learning with local and global consistency. Adv Neural Inf Process Syst 16(16):321\u2013328","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"458_CR8","first-page":"771","volume":"14","author":"J Wang","year":"2013","unstructured":"Wang J, Jebara T, Chang SF (2013) Semi-supervised learning using greedy max-cut. J Mach Learn Res 14(1):771\u2013800","journal-title":"J Mach Learn Res"},{"key":"458_CR9","unstructured":"Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, vol 3, pp 912\u2013919"},{"key":"458_CR10","doi-asserted-by":"crossref","unstructured":"Tang J, Hua XS, Qi GJ, Wang M, Mei T, Wu X (2007) Structure-sensitive manifold ranking for video concept detection. In: Proceedings of the 15th international conference on Multimedia, ACM, pp 852\u2013861","DOI":"10.1145\/1291233.1291430"},{"issue":"4","key":"458_CR11","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1109\/TMM.2008.921853","volume":"10","author":"J Tang","year":"2008","unstructured":"Tang J, Hua XS, Qi GJ, Song Y, Wu X (2008) Video annotation based on kernel linear neighborhood propagation. Multimed IEEE Trans 10(4):620\u2013628","journal-title":"Multimed IEEE Trans"},{"key":"458_CR12","doi-asserted-by":"crossref","unstructured":"Wang M, Mei T, Yuan X, Song Y, Dai LR (2007) Video annotation by graph-based learning with neighborhood similarity. In: Proceedings of the 15th international conference on multimedia, ACM, pp 325\u2013328","DOI":"10.1145\/1291233.1291303"},{"key":"458_CR13","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.knosys.2014.12.014","volume":"76","author":"M Zhao","year":"2015","unstructured":"Zhao M, Chow TW, Zhang Z, Li B (2015) Automatic image annotation via compact graph based semi-supervised learning. Knowl-Based Syst 76:148\u2013165","journal-title":"Knowl-Based Syst"},{"key":"458_CR14","doi-asserted-by":"crossref","unstructured":"Huang L, Wang Y, Liu X, Lang B (2013) Efficient semi-supervised annotation with proxy-based local consistency propagation. In: Multimedia and Expo (ICME), 2013 IEEE international conference on, IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2013.6607453"},{"issue":"2","key":"458_CR15","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/TMM.2011.2174780","volume":"14","author":"S Liu","year":"2012","unstructured":"Liu S, Yan S, Zhang T, Xu C, Liu J, Lu H (2012) Weakly supervised graph propagation towards collective image parsing. Multimed IEEE Trans 14(2):361\u2013373","journal-title":"Multimed IEEE Trans"},{"issue":"10","key":"458_CR16","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/LSP.2014.2329056","volume":"21","author":"M Zhao","year":"2014","unstructured":"Zhao M, Chan RH, Chow TW, Tang P (2014) Compact graph based semi-supervised learning for medical diagnosis in Alzheimer\u2019s disease. Signal Process Lett IEEE 21(10):1192\u20131196","journal-title":"Signal Process Lett IEEE"},{"issue":"1","key":"458_CR17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TKDE.2007.190672","volume":"20","author":"F Wang","year":"2008","unstructured":"Wang F, Zhang C (2008) Label propagation through linear neighborhoods. Knowl Data Eng IEEE Trans 20(1):55\u201367","journal-title":"Knowl Data Eng IEEE Trans"},{"key":"458_CR18","doi-asserted-by":"crossref","unstructured":"Zhuang L, Gao H, Lin Z, Ma Y, Zhang X, Yu N (2012) Non-negative low rank and sparse graph for semi-supervised learning. In: Computer vision and pattern recognition (CVPR), 2012 IEEE conference on, IEEE, pp 2328\u20132335","DOI":"10.1109\/CVPR.2012.6247944"},{"issue":"1","key":"458_CR19","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TKDE.2010.209","volume":"24","author":"B Ni","year":"2012","unstructured":"Ni B, Yan S, Kassim A (2012) Learning a propagable graph for semisupervised learning: classification and regression. Knowl Data Eng IEEE Trans 24(1):114\u2013126","journal-title":"Knowl Data Eng IEEE Trans"},{"key":"458_CR20","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"},{"issue":"8","key":"458_CR21","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. Pattern Anal Mach Intell IEEE Trans 22(8):888\u2013905","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"issue":"9","key":"458_CR22","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/43.159993","volume":"11","author":"L Hagen","year":"1992","unstructured":"Hagen L, Kahng AB (1992) New spectral methods for ratio cut partitioning and clustering. Comput Aid Des Integr Circuit Syst IEEE Trans 11(9):1074\u20131085","journal-title":"Comput Aid Des Integr Circuit Syst IEEE Trans"},{"issue":"5","key":"458_CR23","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/34.857006","volume":"22","author":"S Sarkar","year":"2000","unstructured":"Sarkar S, Soundararajan P (2000) Supervised learning of large perceptual organization: graph spectral partitioning and learning automata. Pattern Anal Mach Intell IEEE Trans 22(5):504\u2013525","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"issue":"11","key":"458_CR24","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1109\/34.244673","volume":"15","author":"Z Wu","year":"1993","unstructured":"Wu Z, Leahy R (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. Pattern Anal Mach Intell IEEE Trans 15(11):1101\u20131113","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"key":"458_CR25","doi-asserted-by":"crossref","unstructured":"Ding CH, He X, Zha H, Gu M, Simon HD (2001) A min\u2013max cut algorithm for graph partitioning and data clustering. In: Data mining, 2001. ICDM 2001, proceedings IEEE international conference on, IEEE, pp 107\u2013114","DOI":"10.1109\/ICDM.2001.989507"},{"key":"458_CR26","unstructured":"Xu L, Li W, Schuurmans D (2009) Fast normalized cut with linear constraints. In: Computer vision and pattern recognition, IEEE conference on, IEEE, pp 2866\u20132873"},{"issue":"11","key":"458_CR27","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TIP.2013.2271865","volume":"22","author":"H Hu","year":"2013","unstructured":"Hu H, Feng J, Yu C, Zhou J (2013) Multi-class constrained normalized cut with hard, soft, unary and pairwise priors and its applications to object segmentation. Image Process IEEE Trans 22(11):4328\u20134340","journal-title":"Image Process IEEE Trans"},{"issue":"1","key":"458_CR28","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1287\/ijoc.1120.0546","volume":"26","author":"YT Yang","year":"2013","unstructured":"Yang YT, Fishbain B, Hochbaum DS, Norman EB, Swanberg E (2013) The supervised normalized cut method for detecting, classifying, and identifying special nuclear materials. INFORMS J Comput 26(1):45\u201358","journal-title":"INFORMS J Comput"},{"issue":"1","key":"458_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10994-008-5084-4","volume":"74","author":"B Kulis","year":"2009","unstructured":"Kulis B, Basu S, Dhillon I, Mooney R (2009) Semi-supervised graph clustering: a kernel approach. Mach Learn 74(1):1\u201322","journal-title":"Mach Learn"},{"issue":"1","key":"458_CR30","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/JETCAS.2014.2298290","volume":"4","author":"W Ren","year":"2014","unstructured":"Ren W, Li G, Tu D, Jia L (2014) Nonnegative matrix factorization with regularizations. Emerg Select Topn Circuit Syst IEEE J 4(1):153\u2013164","journal-title":"Emerg Select Topn Circuit Syst IEEE J"},{"issue":"4","key":"458_CR31","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Luxburg Von","year":"2007","unstructured":"Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395\u2013416","journal-title":"Stat Comput"},{"key":"458_CR32","doi-asserted-by":"crossref","unstructured":"Ding CH, He X, Simon HD (2005) On the equivalence of nonnegative matrix factorization and spectral clustering. In: SDM, vol 5, pp 606\u2013610","DOI":"10.1137\/1.9781611972757.70"},{"key":"458_CR33","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2014.0131","author":"WY Ren","year":"2015","unstructured":"Ren WY, Li GH, Tu D (2015) Graph clustering by congruency approximation. IET Comput Vis. doi: 10.1049\/iet-cvi.2014.0131","journal-title":"IET Comput Vis"},{"issue":"4","key":"458_CR34","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1561\/2200000019","volume":"5","author":"B Kulis","year":"2012","unstructured":"Kulis B (2012) Metric learning: a survey. Found Trend Mach Learn 5(4):287\u2013364","journal-title":"Found Trend Mach Learn"},{"key":"458_CR35","unstructured":"James W, Stein C (1961) Estimation with quadratic loss. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, vol 1 pp 361\u2013379"},{"issue":"4","key":"458_CR36","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1006\/hmat.2000.2289","volume":"27","author":"TH Kjeldsen","year":"2000","unstructured":"Kjeldsen TH (2000) A contextualized historical analysis of the Kuhn\u2013Tucker Theorem in nonlinear programming: the impact of World War II. Hist Math 27(4):331\u2013361","journal-title":"Hist Math"},{"key":"458_CR37","unstructured":"Chapelle O, Zien A (2005) Semi-supervised classification by low density separation. In: Proceedings of the 10th international workshop on artificial intelligence and statistics, vol 1, pp 57\u201364"},{"key":"458_CR38","unstructured":"Niyogi X (2004) Locality preserving projections. In: Neural information processing systems, vol 16, p 153. MIT"},{"issue":"1","key":"458_CR39","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"35","author":"G Liu","year":"2013","unstructured":"Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. Pattern Anal Mach Intell IEEE Trans 35(1):171\u2013184","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"key":"458_CR40","first-page":"1457","volume":"5","author":"PO Hoyer","year":"2004","unstructured":"Hoyer PO (2004) Non-negative matrix factorization with sparseness constraints. J Mach Learn Res 5:1457\u20131469","journal-title":"J Mach Learn Res"},{"key":"458_CR41","unstructured":"Fujiwara Y, Irie G (2014) Efficient label propagation. In: Proceedings of the 31st international conference on machine learning (ICML-14), pp 784\u2013792"},{"issue":"6755","key":"458_CR42","doi-asserted-by":"crossref","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"},{"key":"458_CR43","unstructured":"Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems, pp 556\u2013562"},{"key":"458_CR44","doi-asserted-by":"crossref","unstructured":"Cai D, He X, Wu X, Han J (2008) Non-negative matrix factorization on manifold. In: Data mining, 2008. ICDM\u201908, 8th IEEE international conference on (pp 63\u201372), IEEE","DOI":"10.1109\/ICDM.2008.57"},{"issue":"8","key":"458_CR45","doi-asserted-by":"crossref","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. Pattern Anal Mach Intell IEEE Trans 33(8):1548\u20131560","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"issue":"2","key":"458_CR46","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/0022-247X(68)90078-4","volume":"23","author":"LA Zadeh","year":"1968","unstructured":"Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23(2):421\u2013427","journal-title":"J Math Anal Appl"},{"issue":"5","key":"458_CR47","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1109\/TFUZZ.2014.2371479","volume":"23","author":"XZ Wang","year":"2015","unstructured":"Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638\u20131654","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"458_CR48","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.3233\/IFS-151729","volume":"29","author":"XZ Wang","year":"2015","unstructured":"Wang XZ, Aamir Raza Ashfaq R, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185\u20131196","journal-title":"J Intell Fuzzy Syst"},{"issue":"8","key":"458_CR49","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/TKDE.2011.67","volume":"24","author":"XZ Wang","year":"2012","unstructured":"Wang XZ, Dong LC, Yan JH (2012) Maximum ambiguity-based sample selection in fuzzy decision tree induction. Knowl Data Eng IEEE Trans 24(8):1491\u20131505","journal-title":"Knowl Data Eng IEEE Trans"},{"key":"458_CR50","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379\u2013423, 623\u2013656","journal-title":"Bell Syst Tech J"},{"key":"458_CR51","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1002\/j.1538-7305.1928.tb01236.x","volume":"7","author":"RVL Hartley","year":"1949","unstructured":"Hartley RVL (1949) Transmission of information. Bell Syst Tech J 7:535\u2013563","journal-title":"Bell Syst Tech J"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-015-0458-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-015-0458-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-015-0458-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-015-0458-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T06:28:00Z","timestamp":1567319280000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-015-0458-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,9]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,6]]}},"alternative-id":["458"],"URL":"https:\/\/doi.org\/10.1007\/s13042-015-0458-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2015,11,9]]}}}