{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T14:16:42Z","timestamp":1775312202643,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T00:00:00Z","timestamp":1491868800000},"content-version":"unspecified","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":"crossref","award":["61562003"],"award-info":[{"award-number":["61562003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Jiangxi Province of China","award":["20161BAB202070"],"award-info":[{"award-number":["20161BAB202070"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"crossref","award":["201508360144"],"award-info":[{"award-number":["201508360144"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1007\/s13042-017-0675-7","type":"journal-article","created":{"date-parts":[[2017,4,11]],"date-time":"2017-04-11T02:38:49Z","timestamp":1491878329000},"page":"1707-1717","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Kernel learning and optimization with Hilbert\u2013Schmidt independence criterion"],"prefix":"10.1007","volume":"9","author":[{"given":"Tinghua","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,4,11]]},"reference":[{"key":"675_CR1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel methods for pattern analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge"},{"key":"675_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-015-0383-0","author":"X Gao","year":"2015","unstructured":"Gao X, Fan L, Xu H (2015) Multiple rank multi-linear kernel support vector machine for matrix data classification. Int J Mach Learn Cybern. doi:\n10.1007\/s13042-015-0383-0","journal-title":"Int J Mach Learn Cybern"},{"issue":"13\u201315","key":"675_CR3","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1016\/j.neucom.2009.03.014","volume":"72","author":"T Wang","year":"2009","unstructured":"Wang T, Tian S, Huang H, Deng D (2009) Learning by local kernel polarization. Neurocomputing 72(13\u201315):3077\u20133084","journal-title":"Neurocomputing"},{"key":"675_CR4","first-page":"2211","volume":"12","author":"M G\u00f6nen","year":"2011","unstructured":"G\u00f6nen M, Alpay\u0131n E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211\u20132268","journal-title":"J Mach Learn Res"},{"issue":"5","key":"675_CR5","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1109\/TNNLS.2015.2429682","volume":"27","author":"B Pan","year":"2016","unstructured":"Pan B, Chen WS, Xu C, Chen B (2016) A novel framework for learning geometry-aware kernels. IEEE Trans Neural Netw Learn Syst 27(5):939\u2013951","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"675_CR6","first-page":"489","volume":"20","author":"K Fukumizu","year":"2007","unstructured":"Fukumizu K, Gretton A, Sun X, Sch\u00f6lkopf B (2007) Kernel measures of conditional dependence. Adv Neural Inf Process Syst 20:489\u2013496","journal-title":"Adv Neural Inf Process Syst"},{"key":"675_CR7","first-page":"585","volume":"20","author":"A Gretton","year":"2007","unstructured":"Gretton A, Fukumizu K, Teo CH, Song L, Sch\u00f6lkopf B, Smola A (2007) A kernel statistical test of independence. Adv Neural Inf Process Syst 20:585\u2013592","journal-title":"Adv Neural Inf Process Syst"},{"key":"675_CR8","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton A, Borgwardt KM, Rasch MJ, Sch\u00f6lkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13:723\u2013773","journal-title":"J Mach Learn Res"},{"key":"675_CR9","unstructured":"Chwialkowski K, Gretton A (2014) A kernel independence test of random process. In: Proceedings of the 31th International Conference on Machine Learning, Beijing, China, pp\u00a01422\u20131430"},{"key":"675_CR10","first-page":"1","volume":"3","author":"FR Bach","year":"2002","unstructured":"Bach FR, Jordan MI (2002) Kernel independent component analysis. J Mach Learn Res 3:1\u201348","journal-title":"J Mach Learn Res"},{"key":"675_CR11","unstructured":"Gretton A, Smola A, Bousquet O, Herbrich R, Belitski A, Augath M, Murayama Y, Pauls J, Sch\u00f6lkopf B, Logothetis NK (2005) Kernel constrained covariance for dependence measurement. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, Bridgetown, Barbados, pp\u00a0112\u2013119"},{"key":"675_CR12","doi-asserted-by":"crossref","unstructured":"Gretton A, Bousquet O, Smola A, Sch\u00f6lkopf B (2005) Measuring statistical dependence with Hilbert-Schmidt norms. In: Proceedings of the 16th International Conference on Algorithmic Learning Theory, Singapore, pp\u00a063\u201377","DOI":"10.1007\/11564089_7"},{"key":"675_CR13","doi-asserted-by":"crossref","unstructured":"Song L, Smola A, Gretton A, Borgwardt K (2007) A dependence maximization view of clustering. In: Proceedings of the 24th International Conference on Machine Learning, Corvallis, USA, pp\u00a0823\u2013830","DOI":"10.1145\/1273496.1273599"},{"issue":"10","key":"675_CR14","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/TNN.2010.2064177","volume":"21","author":"W Zhong","year":"2010","unstructured":"Zhong W, Pan W, Kwok JT, Tsang IW (2010) Incorporating the loss function into discriminative clustering of structured outputs. IEEE Trans Neural Netw 21(10):1564\u20131575","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"675_CR15","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/LGRS.2010.2041896","volume":"7","author":"G Camps-Valls","year":"2010","unstructured":"Camps-Valls G, Mooij J, Sch\u00f6lkopf B (2010) Remote sensing feature selection by kernel dependence measures. IEEE Geosci Remote Sens Lett 7(3):587\u2013591","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"675_CR16","first-page":"1393","volume":"13","author":"L Song","year":"2012","unstructured":"Song L, Smola A, Gretton A, Bedo J, Borgwardt K (2012) Feature selection via dependence maximization. J Mach Learn Res 13:1393\u20131434","journal-title":"J Mach Learn Res"},{"key":"675_CR17","doi-asserted-by":"crossref","unstructured":"Chen J, Ji S, Ceran B, Li Q, Wu M, Ye J (2008) Learning subspace kernels for classification. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, USA, pp\u00a0106\u2013114","DOI":"10.1145\/1401890.1401908"},{"key":"675_CR18","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.neucom.2015.05.090","volume":"168","author":"X Shu","year":"2015","unstructured":"Shu X, Lai D, Xu H, Tao L (2015) Learning shared subspace for multi-label dimensionality reduction via dependence maximization. Neurocomputing 168:356\u2013364","journal-title":"Neurocomputing"},{"issue":"1","key":"675_CR19","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1012450327387","volume":"46","author":"O Chapelle","year":"2002","unstructured":"Chapelle O, Vapnik V, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1):131\u2013159","journal-title":"Mach Learn"},{"issue":"5","key":"675_CR20","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1109\/TNN.2002.1031955","volume":"13","author":"SS Keerthi","year":"2002","unstructured":"Keerthi SS (2002) Efficient tuning of SVM hyperparameters using radius\/margin bound and iterative algorithms. IEEE Trans Neural Netw 13(5):1225\u20131229","journal-title":"IEEE Trans Neural Netw"},{"key":"675_CR21","doi-asserted-by":"crossref","unstructured":"Liu Y, Liao S, Hou Y (2011) Learning kernels with upper bounds of leave-one-out error. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, Glasgow, United Kingdom, pp\u00a02205\u20132208","DOI":"10.1145\/2063576.2063927"},{"key":"675_CR22","first-page":"367","volume":"14","author":"N Cristianini","year":"2001","unstructured":"Cristianini N, Shawe-Taylor J, Elisseeff A, Kandola J (2001) On kernel-target alignment. Adv Neural Inf Process Syst 14:367\u2013373","journal-title":"Adv Neural Inf Process Syst"},{"key":"675_CR23","first-page":"795","volume":"13","author":"C Cortes","year":"2012","unstructured":"Cortes C, Mohri M, Rostamizadeh A (2012) Algorithms for learning kernels based on centered alignment. J Mach Learn Res 13:795\u2013828","journal-title":"J Mach Learn Res"},{"issue":"2","key":"675_CR24","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10462-012-9369-4","volume":"43","author":"T Wang","year":"2015","unstructured":"Wang T, Zhao D, Tian S (2015) An overview of kernel alignment and its applications. Artif Intell Rev 43(2):179\u2013192","journal-title":"Artif Intell Rev"},{"issue":"6","key":"675_CR25","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1162\/0899766053630341","volume":"17","author":"Y Baram","year":"2005","unstructured":"Baram Y (2005) Learning by kernel polarization. Neural Comput 17(6):1264\u20131275","journal-title":"Neural Comput"},{"key":"675_CR26","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.knosys.2013.04.006","volume":"48","author":"T Wang","year":"2013","unstructured":"Wang T, Zhao D, Feng Y (2013) Two-stage multiple kernel learning with multiclass kernel polarization. Knowl-Based Syst 48:10\u201316","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"675_CR27","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1016\/j.patcog.2008.04.005","volume":"41","author":"CH Nguyen","year":"2008","unstructured":"Nguyen CH, Ho TB (2008) An efficient kernel matrix evaluation measure. Pattern Recognit 41(11):3366\u20133372","journal-title":"Pattern Recognit"},{"issue":"9","key":"675_CR28","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1109\/TPAMI.2007.70799","volume":"30","author":"L Wang","year":"2008","unstructured":"Wang L (2008) Feature selection with kernel class separability. IEEE Trans Pattern Anal Mach Intell 30(9):1534\u20131546","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"675_CR29","first-page":"67","volume":"2","author":"I Steinwart","year":"2001","unstructured":"Steinwart I (2001) On the influence of the kernels on the consistency of support vector machines. J Mach Learn Res 2:67\u201393","journal-title":"J Mach Learn Res"},{"issue":"10","key":"675_CR30","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1587\/transinf.E95.D.2564","volume":"E95-D","author":"M Sugiyama","year":"2012","unstructured":"Sugiyama M (2012) On kernel parameter selection in Hilbert-Schmidt independence criterion. IEICE Trans Inf Syst E95-D(10):2564\u20132567","journal-title":"IEICE Trans Inf Syst"},{"issue":"11","key":"675_CR31","doi-asserted-by":"crossref","first-page":"3656","DOI":"10.1016\/j.patcog.2014.05.005","volume":"47","author":"Y Lu","year":"2014","unstructured":"Lu Y, Wang L, Lu J, Yang J, Shen C (2014) Multiple kernel clustering based on centered kernel alignment. Pattern Recognit 47(11):3656\u20133664","journal-title":"Pattern Recognit"},{"issue":"1\u20133","key":"675_CR32","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10994-005-1505-9","volume":"61","author":"J Neumann","year":"2005","unstructured":"Neumann J, Schn\u00f6rr C, Steidl G (2005) Combined SVM-based feature selection and classification. Mach Learn 61(1\u20133):129\u2013150","journal-title":"Mach Learn"},{"key":"675_CR33","unstructured":"Lichman M (2013) UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. \nhttp:\/\/archive.ics.uci.edu\/ml\/"},{"key":"675_CR34","doi-asserted-by":"crossref","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27. \nhttp:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm","DOI":"10.1145\/1961189.1961199"},{"issue":"2","key":"675_CR35","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"CW Hsu","year":"2002","unstructured":"Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415\u2013425","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"675_CR36","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/asmb.537","volume":"21","author":"PH Chen","year":"2005","unstructured":"Chen PH, Lin CJ, Ch\u00f6lkopf B (2005) A tutorial on\u2014support vector machines. Appl Stoch Models Bus Ind 21(2):111\u2013136","journal-title":"Appl Stoch Models Bus Ind"},{"key":"675_CR37","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"12","key":"675_CR38","doi-asserted-by":"crossref","first-page":"2791","DOI":"10.1162\/089976602760805296","volume":"14","author":"Z Chen","year":"2002","unstructured":"Chen Z, Haykin S (2002) On different facets of regularization theory. Neural Comput 14(12):2791\u20132846","journal-title":"Neural Comput"},{"issue":"5","key":"675_CR39","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s13042-014-0292-7","volume":"7","author":"P Liu","year":"2016","unstructured":"Liu P, Huang Y, Meng L, Gong S, Zhang G (2016) Two-stage extreme learning machine for high-dimensional data. Int J Mach Learn Cybern 7(5):765\u2013772","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"675_CR40","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s13042-011-0048-6","volume":"3","author":"C Chen","year":"2012","unstructured":"Chen C, Zhang J, He X, Zhou ZH (2012) Non-parametric kernel learning with robust pairwise constraints. Int J Mach Learn Cybern 3(2):83\u201396","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"675_CR41","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/72.991432","volume":"13","author":"CF Lin","year":"2002","unstructured":"Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464\u2013471","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"675_CR42","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1162\/NECO_a_00537","volume":"26","author":"M Yamada","year":"2014","unstructured":"Yamada M, Jitkrittum W, Sigal L, Xing EP, Sugiyama M (2014) High-dimensional feature selection by feature-wise kernelized Lasso. Neural Comput 26(1):185\u2013207","journal-title":"Neural Comput"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-017-0675-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0675-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-017-0675-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,9,5]],"date-time":"2018-09-05T05:00:39Z","timestamp":1536123639000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-017-0675-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,11]]},"references-count":42,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2018,10]]}},"alternative-id":["675"],"URL":"https:\/\/doi.org\/10.1007\/s13042-017-0675-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,11]]}}}