{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:19:58Z","timestamp":1775261998171,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030033378","type":"print"},{"value":"9783030033385","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-03338-5_42","type":"book-chapter","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T23:57:42Z","timestamp":1541116662000},"page":"503-513","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Co-training Approach for Multi-view Density Peak Clustering"],"prefix":"10.1007","author":[{"given":"Yu","family":"Ling","sequence":"first","affiliation":[]},{"given":"Jinrong","family":"He","sequence":"additional","affiliation":[]},{"given":"Silin","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Guoliang","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,3]]},"reference":[{"key":"42_CR1","unstructured":"Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Eighteenth ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp. 1027\u20131035 (2007)"},{"issue":"8","key":"42_CR2","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888\u2013905 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR3","unstructured":"Ester, M., Kriegel, H.P., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226\u2013231. AAAI Press (1996)"},{"issue":"6191","key":"42_CR4","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez, A., Laio, A.: Machine learning. Clustering by fast search and find of density peaks. Science 344(6191), 1492 (2014)","journal-title":"Science"},{"issue":"4","key":"42_CR5","first-page":"209","volume":"41","author":"S Sun","year":"2013","unstructured":"Sun, S.: Multi-view laplacian support vector machines. Appl. Intell. 41(4), 209\u2013222 (2013)","journal-title":"Appl. Intell."},{"issue":"3","key":"42_CR6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"42_CR7","unstructured":"Cohen, J., Cohen, P., West, S.G., et al.: Applied Multiple Regression\/Correlation Analysis for the Behavioral Sciences, 3rd edn, pp. 227\u2013229. L. Erlbaum Associates (2003)"},{"issue":"476","key":"42_CR8","first-page":"1730","volume":"101","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Publ. Am. Stat. Assoc. 101(476), 1730\u20131730 (2004)","journal-title":"Publ. Am. Stat. Assoc."},{"issue":"12","key":"42_CR9","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1162\/0899766042321814","volume":"16","author":"DR Hardoon","year":"2014","unstructured":"Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639\u20132664 (2014)","journal-title":"Neural Comput."},{"issue":"6","key":"42_CR10","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1162\/089976600300015349","volume":"12","author":"JB Tenenbaum","year":"2014","unstructured":"Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Comput. 12(6), 1247\u20131283 (2014)","journal-title":"Neural Comput."},{"key":"42_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/11752790_2","volume-title":"Subspace, Latent Structure and Feature Selection","author":"R Rosipal","year":"2006","unstructured":"Rosipal, R., Kr\u00e4mer, N.: Overview and recent advances in partial least squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34\u201351. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11752790_2"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Sharma, A., Jacobs, D.W.: Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In: Computer Vision and Pattern Recognition, pp. 593\u2013600. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995350"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Sharma, A., Kumar, A., Daume, H., et al.: Generalized multiview analysis: a discriminative latent space. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp. 2160\u20132167 (2012)","DOI":"10.1109\/CVPR.2012.6247923"},{"issue":"12","key":"42_CR14","doi-asserted-by":"publisher","first-page":"3272","DOI":"10.1109\/TCYB.2015.2502248","volume":"46","author":"S Sun","year":"2016","unstructured":"Sun, S., Xie, X., Yang, M.: Multiview uncorrelated discriminant analysis. IEEE Trans. Cybern. 46(12), 3272 (2016)","journal-title":"IEEE Trans. Cybern."},{"key":"42_CR15","series-title":"Breakthroughs in Statistics","first-page":"321","volume-title":"Relations Between Two Sets of Variates","author":"H Hotelling","year":"1992","unstructured":"Hotelling, H.: Relations Between Two Sets of Variates. Breakthroughs in Statistics, pp. 321\u2013377. Springer, New York (1992)"},{"issue":"12","key":"42_CR16","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TPAMI.2015.2417578","volume":"37","author":"C Xu","year":"2015","unstructured":"Xu, C., Tao, D., Xu, C.: Multi-view intact space learning. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2531\u20132544 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Eleventh Conference on Computational Learning Theory, pp. 92\u2013100. ACM (1998)","DOI":"10.1145\/279943.279962"},{"key":"42_CR18","unstructured":"Kumar, A., Daum\u00e9 III, H.: A co-training approach for multi-view spectral clustering. In: International Conference on International Conference on Machine Learning, pp. 393\u2013400. Omnipress (2011)"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, W., Wang, Y., et al.: Co-spectral clustering based density peak. In: IEEE International Conference on Communication Technology, pp. 925\u2013929. IEEE (2015)","DOI":"10.1109\/ICCT.2015.7399974"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Gao, H., Nie, F., Li, X., et al.: Multi-view subspace clustering. In: IEEE International Conference on Computer Vision, pp. 4238\u20134246. IEEE (2016)","DOI":"10.1109\/ICCV.2015.482"},{"key":"42_CR21","unstructured":"Cortes, C., Mohri, M., Rostamizadeh, A.: Learning non-linear combinations of kernels. In: International Conference on Neural Information Processing Systems, pp. 396\u2013404. Curran Associates Inc. (2009)"},{"key":"42_CR22","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"issue":"4","key":"42_CR23","first-page":"852","volume":"61","author":"CD Manning","year":"2008","unstructured":"Manning, C.D., Raghavan, P., Sch\u00fctze, H.: An introduction to information retrieval. J. Am. Soc. Inf. Sci. Technol. 61(4), 852\u2013853 (2008)","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"issue":"1","key":"42_CR24","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193\u2013218 (1985). Assortative pairing and life history strategy - a cross-cultural study. Hum. Nat. 20, 317\u2013330","journal-title":"J. Classif."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03338-5_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T23:05:48Z","timestamp":1775257548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-03338-5_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030033378","9783030033385"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03338-5_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"3 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/prcv.qyhw.net.cn\/?lang=en&meeting_id=255","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}