{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T08:07:13Z","timestamp":1648541233003},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2014,10,15]],"date-time":"2014-10-15T00:00:00Z","timestamp":1413331200000},"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 Process Lett"],"published-print":{"date-parts":[[2015,12]]},"DOI":"10.1007\/s11063-014-9385-2","type":"journal-article","created":{"date-parts":[[2014,10,14]],"date-time":"2014-10-14T11:11:13Z","timestamp":1413285073000},"page":"715-744","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection"],"prefix":"10.1007","volume":"42","author":[{"given":"Zhe","family":"Wang","sequence":"first","affiliation":[]},{"given":"Qi","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Wenbo","family":"Jie","sequence":"additional","affiliation":[]},{"given":"Daqi","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,10,15]]},"reference":[{"issue":"4","key":"9385_CR1","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/S0022-0000(03)00025-4","volume":"66","author":"D Achlioptas","year":"2003","unstructured":"Achlioptas D (2003) Database-friendly random projections: johnson-lindenstrauss with binary coins. J Comput Syst Sci 66(4):671\u2013687","journal-title":"J Comput Syst Sci"},{"issue":"2","key":"9385_CR2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10994-006-6265-7","volume":"63","author":"R Arriaga","year":"2006","unstructured":"Arriaga R, Vempala S (2006) An algorithmic theory of learning: robust concepts and random projection. Mach Learn 63(2):161\u2013182","journal-title":"Mach Learn"},{"key":"9385_CR3","doi-asserted-by":"crossref","unstructured":"Bach FR, Lanckriet GR, Jordan MI (2004) Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the twenty-first international conference on machine learning. ACM, p 6","DOI":"10.1145\/1015330.1015424"},{"key":"9385_CR4","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository [ http:\/\/archive.ics.uci.edu\/ml ]"},{"issue":"1","key":"9385_CR5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10994-006-7550-1","volume":"65","author":"M Balcan","year":"2006","unstructured":"Balcan M, Blum A, Vempala S (2006) Kernels as features: on kernels, margins, and low-dimensional mappings. Mach Learn 65(1):79\u201394","journal-title":"Mach Learn"},{"key":"9385_CR6","first-page":"463","volume":"3","author":"P Bartlett","year":"2003","unstructured":"Bartlett P, Mendelson S (2003) Rademacher and gaussian complexities: risk bounds and structural results. J Mach Lear Res 3:463\u2013482","journal-title":"J Mach Lear Res"},{"key":"9385_CR7","doi-asserted-by":"crossref","unstructured":"Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. pp 245\u2013250","DOI":"10.1145\/502512.502546"},{"key":"9385_CR8","unstructured":"Boutsidis C, Zouzias A, Drineas P (2010) Random projections for $$k$$ k -means clustering. arXiv preprint arXiv:1011.4632"},{"key":"9385_CR9","doi-asserted-by":"crossref","unstructured":"Calderon-Niquin M, Valverde-Rebaza J (2012) Multiple kernel learning based on local and nonlinear combinations. In: 2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI). IEEE, pp 1\u20137","DOI":"10.1109\/CLEI.2012.6427179"},{"issue":"2","key":"9385_CR10","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","volume":"52","author":"EJ Cand\u00e8s","year":"2006","unstructured":"Cand\u00e8s EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489\u2013509","journal-title":"IEEE Trans Inf Theory"},{"key":"9385_CR11","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.sigpro.2013.06.016","volume":"94","author":"X Chen","year":"2014","unstructured":"Chen X, Qi C (2014) Nonlinear neighbor embedding for single image super-resolution via kernel mapping. Signal Proc 94:6\u201322","journal-title":"Signal Proc"},{"issue":"10","key":"9385_CR12","doi-asserted-by":"crossref","first-page":"12151","DOI":"10.1016\/j.eswa.2011.03.025","volume":"38","author":"Z Chen","year":"2011","unstructured":"Chen Z, Li J, Wei L, Xu W, Shi Y (2011) Multiple-kernel svm based multiple-task oriented data mining system for gene expression data analysis. Expert Syst Appl 38(10):12151\u201312159","journal-title":"Expert Syst Appl"},{"key":"9385_CR13","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge university press, New York"},{"issue":"1","key":"9385_CR14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1002\/rsa.10073","volume":"22","author":"S Dasgupta","year":"2002","unstructured":"Dasgupta S, Gupta A (2002) An elementary proof of the johnson-lindenstrauss lemma. Random Struct Algorithm 22(1):60\u201365","journal-title":"Random Struct Algorithm"},{"key":"9385_CR15","first-page":"355","volume":"18","author":"J Farquhar","year":"2005","unstructured":"Farquhar J, Hardoon D, Meng H, Shawe-taylor J, Szedmak S (2005) Two view learning: Svm-2k, theory and practice. Adv Neural Inf Proc Syst 18:355\u2013362","journal-title":"Adv Neural Inf Proc Syst"},{"key":"9385_CR16","doi-asserted-by":"crossref","unstructured":"Goel N, Bebis G, Nefian A (2005) Face recognition experiments with random projection. In: Defense and Security. International Society for Optics and Photonics, pp 426\u2013437","DOI":"10.1117\/12.605553"},{"key":"9385_CR17","doi-asserted-by":"crossref","unstructured":"Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: Advances in information retrieval. Springer, pp 345\u2013359","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"9385_CR18","doi-asserted-by":"crossref","unstructured":"Hino H (2013) Gaussian multiple kernel learning with entropy power inequality. In: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp 1\u20136","DOI":"10.1109\/MLSP.2013.6661956"},{"key":"9385_CR19","doi-asserted-by":"crossref","unstructured":"Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on Theory of computing. pp 604\u2013613","DOI":"10.1145\/276698.276876"},{"key":"9385_CR20","volume-title":"Linear discriminant analysis","author":"AJ Izenman","year":"2008","unstructured":"Izenman AJ (2008) Linear discriminant analysis. Springer, New York"},{"key":"9385_CR21","doi-asserted-by":"crossref","unstructured":"Johnson W, Lindenstrauss J (1984) Extensions of Lipschitz mappings into a Hilbert space. In: Conference in modern analysis and probability (New Haven, Conn., 1982), volume 26. American Mathematical Society, pp 189\u2013206","DOI":"10.1090\/conm\/026\/737400"},{"key":"9385_CR22","unstructured":"Kaski S (1997) Data exploration using self-organizing maps. In: Acta Polytechnica Scandinavica: Mathematics, Computing and Management in Engineering Series NO. 82. Citeseer"},{"key":"9385_CR23","doi-asserted-by":"crossref","unstructured":"Kim SJ, Magnani A, Boyd S (2006) Optimal kernel selection in kernel fisher discriminant analysis. In: Proceedings of the 23rd international conference on Machine learning. pp 465\u2013472","DOI":"10.1145\/1143844.1143903"},{"issue":"5","key":"9385_CR24","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1109\/18.930926","volume":"47","author":"V Koltchinskii","year":"2001","unstructured":"Koltchinskii V (2001) Rademacher penalties and structural risk minimization. IEEE Trans Inf Theory 47(5):1902\u20131914","journal-title":"IEEE Trans Inf Theory"},{"key":"9385_CR25","doi-asserted-by":"crossref","unstructured":"Koltchinskii V, Panchenko D (2000) Rademacher processes and bounding the risk of function learning. In: High Dimensional Probability II, volume 47. Springer, pp 443\u2013457","DOI":"10.1007\/978-1-4612-1358-1_29"},{"key":"9385_CR26","unstructured":"Kressel UHG (1999) Advances in kernel methods. In: Pairwise Classification and Support Vector Machines. MIT Press, pp 255\u2013268"},{"key":"9385_CR27","first-page":"27","volume":"5","author":"GRG Lanckriet","year":"2004","unstructured":"Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5:27\u201372","journal-title":"J Mach Learn Res"},{"key":"9385_CR28","first-page":"259","volume":"25","author":"T Landauer","year":"1998","unstructured":"Landauer T, Foltz P, Laham D (1998) An introduction to latent semantic analysis. J Mach Learn Res 25:259\u2013284","journal-title":"J Mach Learn Res"},{"issue":"3","key":"9385_CR29","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11063-012-9234-0","volume":"36","author":"J Liang","year":"2012","unstructured":"Liang J, Chen L, Chen X (2012) Discriminant kernel learning using hybrid regularization. Neural Proc Lett 36(3):257\u2013273","journal-title":"Neural Proc Lett"},{"key":"9385_CR30","unstructured":"Liang Z, Liu N (2013) Efficient feature scaling for support vector machines with a quadratic kernel. Neural Processing Letters pp 1\u201312"},{"issue":"2","key":"9385_CR31","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1006\/jmbi.1997.0948","volume":"268","author":"M Linial","year":"1997","unstructured":"Linial M, Linial N, Tishby N, Yona G (1997) Global self-organization of all known protein sequences reveals inherent biological signatures1. J Mole Biol 268(2):539\u2013556","journal-title":"J Mole Biol"},{"issue":"2","key":"9385_CR32","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TSMCB.2012.2212243","volume":"43","author":"X Liu","year":"2013","unstructured":"Liu X, Wang L, Yin J, Zhu E, Zhang J (2013) An efficient approach to integrating radius information into multiple kernel learning. IEEE Trans Cybern 43(2):557\u2013569","journal-title":"IEEE Trans Cybern"},{"issue":"14","key":"9385_CR33","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1016\/S0167-8655(03)00054-0","volume":"24","author":"J Lkeski","year":"2003","unstructured":"Lkeski J (2003) Ho-kashyap classifier with generalization control. Pattern Recognition Letters 24(14):2281\u20132290","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"9385_CR34","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TNN.2002.806629","volume":"14","author":"J Lu","year":"2003","unstructured":"Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Netw 14(1):117\u2013126","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"9385_CR35","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/18.971753","volume":"48","author":"S Mendelson","year":"2002","unstructured":"Mendelson S (2002) Rademacher averages and phase transitions in glivenko-cantelli classes. IEEE Trans Inf Theory 48(1):251\u2013263","journal-title":"IEEE Trans Inf Theory"},{"key":"9385_CR36","doi-asserted-by":"crossref","unstructured":"Papadimitriou CH, Tamaki H, Raghavan P, Vempala S (1998) Latent semantic indexing: A probabilistic analysis. In: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems. pp 159\u2013168","DOI":"10.1145\/275487.275505"},{"issue":"82","key":"9385_CR37","first-page":"1","volume":"18","author":"M Rudelson","year":"2013","unstructured":"Rudelson M, Vershynin R (2013) Hanson-wright inequality and sub-gaussian concentration. Electron Commun Prob 18(82):1\u20139","journal-title":"Electron Commun Prob"},{"issue":"5","key":"9385_CR38","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/72.788641","volume":"10","author":"B Scholkopf","year":"1999","unstructured":"Scholkopf B, Mika S, Burges CJC, Knirsch P, Muller KR, Ratsch G, Smola AJ (1999) Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw 10(5):1000\u20131017","journal-title":"IEEE Trans Neural Netw"},{"key":"9385_CR39","first-page":"1273","volume":"18","author":"S Sonnenburg","year":"2006","unstructured":"Sonnenburg S, R\u00e4tsch G, Sch\u00e4fer C (2006) A general and efficient multiple kernel learning algorithm. 18:1273\u20131280","journal-title":"A general and efficient multiple kernel learning algorithm."},{"key":"9385_CR40","first-page":"1531","volume":"7","author":"S Sonnenburg","year":"2006","unstructured":"Sonnenburg S, R\u00e4tsch G, Sch\u00e4fer C, Sch\u00f6lkopf B (2006) Large scale multiple kernel learning. The Journal of Machine Learning Research 7:1531\u20131565","journal-title":"The Journal of Machine Learning Research"},{"issue":"6","key":"9385_CR41","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TSMCB.2005.850183","volume":"35","author":"G Valentini","year":"2005","unstructured":"Valentini G (2005) An experimental bias-variance analysis of svm ensembles based on resampling techniques. IEEE Trans Syst Man Cybern Part B 35(6):1252\u20131271","journal-title":"IEEE Trans Syst Man Cybern Part B"},{"issue":"2","key":"9385_CR42","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TPAMI.2007.70786","volume":"30","author":"Z Wang","year":"2008","unstructured":"Wang Z, Chen S, Sun T (2008) Multik-mhks: a novel multiple kernel learning algorithm. IEEE Trans Pattern Anal Mach Intell 30(2):348\u2013353","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9385_CR43","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.knosys.2012.08.017","volume":"37","author":"Z Wang","year":"2013","unstructured":"Wang Z, Jie W, Chen S, Gao D (2013) Random projection ensemble learning with multiple empirical kernels. Knowledge-Based Syst 37:388\u2013393","journal-title":"Knowledge-Based Syst"},{"key":"9385_CR44","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.neucom.2013.03.019","volume":"119","author":"Z Wang","year":"2013","unstructured":"Wang Z, Jie W, Gao D (2013) A novel multiple nystr\u00f6m-approximating kernel discriminant analysis. Neurocomputing 119:385\u2013398","journal-title":"Neurocomputing"},{"issue":"7\u20138","key":"9385_CR45","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1007\/s00521-012-1161-5","volume":"23","author":"Z Wang","year":"2013","unstructured":"Wang Z, Xu J, Gao D, Fu Y (2013) Multiple empirical kernel learning based on local information. Neural Comput Appl 23(7\u20138):2113\u20132120","journal-title":"Neural Comput Appl"},{"key":"9385_CR46","unstructured":"Welling M (2005) Fisher linear discriminant analysis. Department of Computer Science, University of Toronto, 3"},{"key":"9385_CR47","doi-asserted-by":"crossref","unstructured":"Wu P, Duan F, Guo P (2013) Multiple kernel learning method using mrmr criterion and kernel alignment. In: Neural Information Processing. Springer, pp 113\u2013120","DOI":"10.1007\/978-3-642-42054-2_15"},{"key":"9385_CR48","doi-asserted-by":"crossref","unstructured":"Xiong H (2009) A unified framework for kernelization: The empirical kernel feature space. In: Chinese Conference on Pattern Recognition 2009 (CCPR 2009). IEEE, pp 1\u20135","DOI":"10.1109\/CCPR.2009.5344130"},{"issue":"1","key":"9385_CR49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(00)00122-2","volume":"56","author":"QS Xu","year":"2001","unstructured":"Xu QS, Liang YZ (2001) Monte carlo cross validation. Chemom Intell Lab Syst 56(1):1\u201311","journal-title":"Chemom Intell Lab Syst"},{"issue":"5","key":"9385_CR50","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TNNLS.2012.2237183","volume":"24","author":"X Xu","year":"2013","unstructured":"Xu X, Tsang IW, Xu D (2013) Soft margin multiple kernel learning. IEEE Trans Neural Netw Learn Syst 24(5):749\u2013761","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9385_CR51","doi-asserted-by":"crossref","unstructured":"Yan F, Mikolajczyk K, Barnard M, Cai H, Kittler J (2010) lp-norm multiple kernel fisher discriminant analysis for object and image categorisation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition. pp 3626\u20133632","DOI":"10.1109\/CVPR.2010.5539916"},{"key":"9385_CR52","doi-asserted-by":"crossref","unstructured":"Yang B, Bu Y (2009) Multiple kernel learning using regularized ho-kashyap classifier in empirical kernel mapping space. In: Fifth International Conference on Natural Computation (ICNC\u201909), volume 1. IEEE, pp 209\u2013212","DOI":"10.1109\/ICNC.2009.265"},{"issue":"3","key":"9385_CR53","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TNN.2010.2103571","volume":"22","author":"H Yang","year":"2011","unstructured":"Yang H, Xu Z, Ye J, King I, Lyu MR (2011) Efficient sparse generalized multiple kernel learning. IEEE Trans Neural Netw 22(3):433\u2013446","journal-title":"IEEE Trans Neural Netw"},{"key":"9385_CR54","first-page":"483","volume":"6","author":"J Ye","year":"2005","unstructured":"Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483\u2013502","journal-title":"J Mach Learn Res"},{"issue":"4","key":"9385_CR55","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/TCBB.2004.45","volume":"1","author":"J Ye","year":"2004","unstructured":"Ye J, Li T, Xiong T, Janardan R (2004) Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE\/ACM Trans Comput Biol Bioinform 1(4):181\u2013190","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-014-9385-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-014-9385-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-014-9385-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T01:28:12Z","timestamp":1565918892000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-014-9385-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,10,15]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2015,12]]}},"alternative-id":["9385"],"URL":"https:\/\/doi.org\/10.1007\/s11063-014-9385-2","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,15]]}}}