{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:18Z","timestamp":1740108018427,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2018,5,5]],"date-time":"2018-05-05T00:00:00Z","timestamp":1525478400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s00521-018-3491-4","type":"journal-article","created":{"date-parts":[[2018,5,5]],"date-time":"2018-05-05T11:08:48Z","timestamp":1525518528000},"page":"6821-6842","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiple-relations-constrained image classification with limited training samples via Pareto optimization"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7191-3400","authenticated-orcid":false,"given":"Di","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9548-0411","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yajun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,5,5]]},"reference":[{"key":"3491_CR1","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1109\/TCYB.2014.2307862","volume":"44","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Tang YY, Tao D (2014) High-order distance-based multiview stochastic learning in image classification. IEEE Trans Cybern 44:2431\u20132442","journal-title":"IEEE Trans Cybern"},{"key":"3491_CR2","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1109\/TGRS.2015.2481938","volume":"54","author":"J Xia","year":"2016","unstructured":"Xia J, Chanussot J, Du P, He X (2016) Rotation-based support vector machine ensemble in classification of hyperspectral data with limited training samples. IEEE Trans Geosci Remote Sens 54:1519\u20131531","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3491_CR3","doi-asserted-by":"publisher","first-page":"4052","DOI":"10.1109\/TGRS.2016.2535538","volume":"54","author":"C Zhao","year":"2016","unstructured":"Zhao C, Gao X, Wang Y, Li J (2016) Efficient multiple-feature learning-based hyperspectral image classification with limited training samples. IEEE Trans Geosci Remote Sens 54:4052\u20134062","journal-title":"IEEE Trans Geosci Remote Sens"},{"doi-asserted-by":"crossref","unstructured":"Caruana R (1998) Multitask learning. In: Learning to learn. Springer, Berlin, pp 95\u2013133","key":"3491_CR4","DOI":"10.1007\/978-1-4615-5529-2_5"},{"key":"3491_CR5","first-page":"615","volume":"6","author":"T Evgeniou","year":"2005","unstructured":"Evgeniou T, Micchelli CA, Pontil M (2005) Learning multiple tasks with kernel methods. J Mach Learn Res 6:615\u2013637","journal-title":"J Mach Learn Res"},{"unstructured":"He J, Lawrence R (2011) A graph-based framework for multi-task multi-view learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp 25\u201332","key":"3491_CR6"},{"key":"3491_CR7","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1002\/hbm.23575","volume":"38","author":"J Wang","year":"2017","unstructured":"Wang J, Wang Q, Peng J, Nie D, Zhao F, Kim M et al (2017) Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study. Hum Brain Mapp 38:3081\u20133097","journal-title":"Hum Brain Mapp"},{"key":"3491_CR8","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46:450\u2013461","journal-title":"IEEE Trans Cybern"},{"doi-asserted-by":"crossref","unstructured":"Guillaumin M, Verbeek J, Schmid C (2010) Multimodal semi-supervised learning for image classification. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 902\u2013909","key":"3491_CR9","DOI":"10.1109\/CVPR.2010.5540120"},{"doi-asserted-by":"crossref","unstructured":"Yan Y, Ricci E, Subramanian R, Lanz O, Sebe N (2013) No matter where you are: flexible graph-guided multi-task learning for multi-view head pose classification under target motion. In: Proceedings of the IEEE international conference on computer vision, pp 1177\u20131184","key":"3491_CR10","DOI":"10.1109\/ICCV.2013.150"},{"key":"3491_CR11","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 (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process 22:523\u2013536","journal-title":"IEEE Trans Image Process"},{"key":"3491_CR12","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.1109\/TIP.2012.2205006","volume":"21","author":"X-T Yuan","year":"2012","unstructured":"Yuan X-T, Liu X, Yan S (2012) Visual classification with multitask joint sparse representation. IEEE Trans Image Process 21:4349\u20134360","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"3491_CR13","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1109\/TPAMI.2007.1055","volume":"29","author":"A Torralba","year":"2007","unstructured":"Torralba A, Murphy KP, Freeman WT (2007) Sharing visual features for multiclass and multiview object detection. IEEE Trans Pattern Anal Mach Intell 29(5):854\u2013869","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"doi-asserted-by":"crossref","unstructured":"Zhang J, Huan J (2012) Inductive multi-task learning with multiple view data. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 543\u2013551","key":"3491_CR14","DOI":"10.1145\/2339530.2339617"},{"doi-asserted-by":"crossref","unstructured":"Jin X, Zhuang F, Wang S, He Q, Shi Z (2013) Shared structure learning for multiple tasks with multiple views. In: Joint European conference on machine learning and knowledge discovery in databases, pp 353\u2013368","key":"3491_CR15","DOI":"10.1007\/978-3-642-40991-2_23"},{"doi-asserted-by":"crossref","unstructured":"Yang P, He J (2015) A graph-based hybrid framework for modeling complex heterogeneity. In: 2015 IEEE international conference on data mining (ICDM), pp 1081\u20131086","key":"3491_CR16","DOI":"10.1109\/ICDM.2015.109"},{"key":"3491_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/2792984","volume":"48","author":"B Li","year":"2015","unstructured":"Li B, Li J, Tang K, Yao X (2015) Many-objective evolutionary algorithms: a survey. ACM Comput Surv (CSUR) 48:13","journal-title":"ACM Comput Surv (CSUR)"},{"key":"3491_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182\u2013197","journal-title":"IEEE Trans Evol Comput"},{"key":"3491_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/S0309-1708(01)00020-3","volume":"25","author":"M Erickson","year":"2002","unstructured":"Erickson M, Mayer A, Horn J (2002) Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA). Adv Water Resour 25:51\u201365","journal-title":"Adv Water Resour"},{"key":"3491_CR20","first-page":"287","volume":"2","author":"M Reyes-Sierra","year":"2006","unstructured":"Reyes-Sierra M, Coello CAC (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2:287\u2013308","journal-title":"Int J Comput Intell Res"},{"doi-asserted-by":"crossref","unstructured":"Nebro AJ, Durillo JJ, Garcia-Nieto J, Coello CC, Luna F, Alba E (2009) SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE symposium on computational intelligence in multi-criteria decision-making. mcdm\u201909, pp 66\u201373","key":"3491_CR21","DOI":"10.1109\/MCDM.2009.4938830"},{"key":"3491_CR22","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TEVC.2007.894202","volume":"12","author":"Q Zhang","year":"2008","unstructured":"Zhang Q, Zhou A, Jin Y (2008) RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans Evol Comput 12:41\u201363","journal-title":"IEEE Trans Evol Comput"},{"doi-asserted-by":"crossref","unstructured":"Okabe T, Jin Y, Sendoff B, Olhofer M (2004) Voronoi-based estimation of distribution algorithm for multi-objective optimization. In: Congress on evolutionary computation. CEC2004, pp 1594\u20131601","key":"3491_CR23","DOI":"10.1109\/CEC.2004.1331086"},{"key":"3491_CR24","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1162\/evco.2010.18.1.18105","volume":"18","author":"A Elhossini","year":"2010","unstructured":"Elhossini A, Areibi S, Dony R (2010) Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization. Evol Comput 18:127\u2013156","journal-title":"Evol Comput"},{"key":"3491_CR25","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1109\/TSMCB.2006.887946","volume":"37","author":"B-B Li","year":"2007","unstructured":"Li B-B, Wang L (2007) A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling. IEEE Trans Syst Man Cybern Part B (Cybern) 37:576\u2013591","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"3491_CR26","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s11047-009-9171-7","volume":"9","author":"MA Abido","year":"2010","unstructured":"Abido MA (2010) Multiobjective particle swarm optimization with nondominated local and global sets. Nat Comput 9:747\u2013766","journal-title":"Nat Comput"},{"key":"3491_CR27","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1016\/j.ins.2009.01.005","volume":"179","author":"Y Wang","year":"2009","unstructured":"Wang Y, Yang Y (2009) Particle swarm optimization with preference order ranking for multi-objective optimization. Inf Sci 179:1944\u20131959","journal-title":"Inf Sci"},{"unstructured":"Van Den Bergh F (2006) An analysis of particle swarm optimizers. University of Pretoria","key":"3491_CR28"},{"key":"3491_CR29","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1287\/moor.6.1.19","volume":"6","author":"FJ Solis","year":"1981","unstructured":"Solis FJ, Wets RJB (1981) Minimization by random search techniques. Math Oper Res 6:19\u201330","journal-title":"Math Oper Res"},{"unstructured":"Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Congress on evolutionary computation","key":"3491_CR30"},{"key":"3491_CR31","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.ins.2012.01.005","volume":"193","author":"J Sun","year":"2012","unstructured":"Sun J, Wu X, Palade V, Fang W, Lai C-H, Xu W (2012) Convergence analysis and improvements of quantum-behaved particle swarm optimization. Inf Sci 193:81\u2013103","journal-title":"Inf Sci"},{"unstructured":"Banka H, Dara S (2014) Hamming distance based binary PSO for feature selection and classification from high dimensional gene expression data. In: IWBBIO, pp 507\u2013514","key":"3491_CR32"},{"key":"3491_CR33","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.1016\/j.patcog.2015.04.005","volume":"48","author":"S Ding","year":"2015","unstructured":"Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recognit 48:2993\u20133003","journal-title":"Pattern Recognit"},{"key":"3491_CR34","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1162\/EVCO_a_00049","volume":"20","author":"J Sun","year":"2012","unstructured":"Sun J, Fang W, Wu X, Palade V, Xu W (2012) Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evol Comput 20:349\u2013393","journal-title":"Evol Comput"},{"key":"3491_CR35","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/TMAG.2007.916032","volume":"44","author":"L Santos Coelho dos","year":"2008","unstructured":"dos Santos Coelho L, Alotto P (2008) Global optimization of electromagnetic devices using an exponential quantum-behaved particle swarm optimizer. IEEE Trans Magn 44:1074\u20131077","journal-title":"IEEE Trans Magn"},{"key":"3491_CR36","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jtbi.2008.05.010","volume":"254","author":"Y Cai","year":"2008","unstructured":"Cai Y, Sun J, Wang J, Ding Y, Tian N, Liao X et al (2008) Optimizing the codon usage of synthetic gene with QPSO algorithm. J Theor Biol 254:123\u2013127","journal-title":"J Theor Biol"},{"key":"3491_CR37","doi-asserted-by":"publisher","first-page":"4232","DOI":"10.1016\/j.eswa.2009.11.079","volume":"37","author":"C Sun","year":"2010","unstructured":"Sun C, Lu S (2010) Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization. Expert Syst Appl 37:4232\u20134241","journal-title":"Expert Syst Appl"},{"unstructured":"Chua T-S, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM international conference on image and video retrieval, p 48","key":"3491_CR38"},{"unstructured":"Martino AD, Castellanos FX, Anderson J, Alaerts K, Assaf M, Behrmann et al (2012) The Autism Brain Imaging Data Exchange (ABIDE) consortium: open sharing of autism resting state fMRI data","key":"3491_CR39"},{"key":"3491_CR40","first-page":"27","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:27","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"3491_CR41","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","volume":"55","author":"D Zhang","year":"2011","unstructured":"Zhang D, Wang Y, Zhou L, Yuan H, Shen D, Alzheimer\u2019s Disease Neuroimaging Initiative (2011) Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment. Neuroimage 55:856\u2013867","journal-title":"Neuroimage"},{"unstructured":"Zhou J, Chen J, Ye J (2011) Malsar: multi-task learning via structural regularization. Arizona State University","key":"3491_CR42"},{"unstructured":"Sierra MR, Coello CAC (2005) Improving PSO-based multi-objective optimization using crowding, mutation and \u2208-dominance. In: Evolutionary multi-criterion optimization, pp 505\u2013519","key":"3491_CR43"},{"unstructured":"Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm","key":"3491_CR44"},{"key":"3491_CR45","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang Q, Li H (2007) MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11:712\u2013731","journal-title":"IEEE Trans Evol Comput"},{"doi-asserted-by":"crossref","unstructured":"Durillo JJ, Garc\u00eda-Nieto J, Nebro AJ, Coello CAC, Luna F, Alba E (2009) Multi-objective particle swarm optimizers: an experimental comparison. In: International conference on evolutionary multi-criterion optimization, pp 495\u2013509","key":"3491_CR46","DOI":"10.1007\/978-3-642-01020-0_39"},{"key":"3491_CR47","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/TMM.2014.2299516","volume":"16","author":"S Gao","year":"2014","unstructured":"Gao S, Chia L-T, Tsang IW-H, Ren Z (2014) Concurrent single-label image classification and annotation via efficient multi-layer group sparse coding. IEEE Trans Multimed 16:762\u2013771","journal-title":"IEEE Trans Multimed"},{"doi-asserted-by":"crossref","unstructured":"Gao S, Chia L-T, Tsang IW-H (2011) Multi-layer group sparse coding\u2014for concurrent image classification and annotation. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR), pp 2809\u20132816","key":"3491_CR48","DOI":"10.1109\/CVPR.2011.5995454"},{"unstructured":"Feng Y, Xiao J, Zhuang Y, Liu X (2012) Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Asian conference on computer vision, pp 343\u2013357","key":"3491_CR49"},{"key":"3491_CR50","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/TII.2016.2605629","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Cao X, Ho JK, Chow TW (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13:520\u2013531","journal-title":"IEEE Trans Ind Inf"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3491-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3491-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3491-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,20]],"date-time":"2019-10-20T16:49:19Z","timestamp":1571590159000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3491-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,5]]},"references-count":50,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["3491"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3491-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2018,5,5]]},"assertion":[{"value":"27 July 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2018","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":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}