{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:17:55Z","timestamp":1743124675396,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981441"},{"type":"electronic","value":"9789819981458"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8145-8_9","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:21Z","timestamp":1701039741000},"page":"101-112","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Incompleteness to\u00a0Unity: A Framework for\u00a0Multi-view Clustering with\u00a0Missing Values"],"prefix":"10.1007","author":[{"given":"Fangchen","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenye","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Balzano, L., Nowak, R., Recht, B.: Online identification and tracking of subspaces from highly incomplete information. In: 2010 48th Annual Allerton conference on Communication, Control, and Computing (Allerton), pp. 704\u2013711. IEEE (2010)","DOI":"10.1109\/ALLERTON.2010.5706976"},{"issue":"3","key":"9_CR2","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1006\/jath.1994.1136","volume":"79","author":"HH Bauschke","year":"1994","unstructured":"Bauschke, H.H., Borwein, J.M.: Dykstra\u2019s alternating projection algorithm for two sets. J. Approx. Theory 79(3), 418\u2013443 (1994)","journal-title":"J. Approx. Theory"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Berry, M.W., Mezher, D., Philippe, B., Sameh, A.: Parallel algorithms for the singular value decomposition. In: Handbook of Parallel Computing and Statistics, pp. 133\u2013180. Chapman and Hall\/CRC (2005)","DOI":"10.1201\/9781420028683.ch4"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Boyle, J.P., Dykstra, R.L.: A method for finding projections onto the intersection of convex sets in Hilbert spaces. In: Advances in Order Restricted Statistical Inference, pp. 28\u201347. Springer, New York (1986). https:\/\/doi.org\/10.1007\/978-1-4613-9940-7_3","DOI":"10.1007\/978-1-4613-9940-7_3"},{"issue":"4","key":"9_CR5","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1137\/080738970","volume":"20","author":"JF Cai","year":"2010","unstructured":"Cai, J.F., Cand\u00e8s, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956\u20131982 (2010)","journal-title":"SIAM J. Optim."},{"issue":"6","key":"9_CR6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1145\/2184319.2184343","volume":"55","author":"E Candes","year":"2012","unstructured":"Candes, E., Recht, B.: Exact matrix completion via convex optimization. Commun. ACM 55(6), 111\u2013119 (2012)","journal-title":"Commun. ACM"},{"key":"9_CR7","unstructured":"Du, L., et al.: Robust multiple kernel K-means using L21-Norm. In: 24th International Joint Conference on Artificial Intelligence (2015)"},{"issue":"384","key":"9_CR8","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1080\/01621459.1983.10477029","volume":"78","author":"RL Dykstra","year":"1983","unstructured":"Dykstra, R.L.: An algorithm for restricted least squares regression. J. Am. Stat. Assoc. 78(384), 837\u2013842 (1983)","journal-title":"J. Am. Stat. Assoc."},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Fan, J., Udell, M.: Online high rank matrix completion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8690\u20138698 (2019)","DOI":"10.1109\/CVPR.2019.00889"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Guo, J., Ye, J.: Anchors bring ease: an embarrassingly simple approach to partial multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 118\u2013125 (2019)","DOI":"10.1609\/aaai.v33i01.3301118"},{"issue":"2","key":"9_CR11","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1137\/090771806","volume":"53","author":"N Halko","year":"2011","unstructured":"Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53(2), 217\u2013288 (2011)","journal-title":"SIAM Rev."},{"key":"9_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100799","volume":"27","author":"MK Hasan","year":"2021","unstructured":"Hasan, M.K., Alam, M.A., Roy, S., Dutta, A., Jawad, M.T., Das, S.: Missing value imputation affects the performance of machine learning: a review and analysis of the literature (2010\u20132021). Inf. Med. Unlocked 27, 100799 (2021)","journal-title":"Inf. Med. Unlocked"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Huang, H.C., Chuang, Y.Y., Chen, C.S.: Affinity aggregation for spectral clustering. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 773\u2013780. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247748"},{"key":"9_CR14","unstructured":"Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems 24 (2011)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Li, S.Y., Jiang, Y., Zhou, Z.H.: Partial multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8973"},{"key":"9_CR16","unstructured":"Li, W.: Estimating jaccard index with missing observations: a matrix calibration approach. In: Advances in Neural Information Processing Systems, vol. 28, pp. 2620\u20132628. Canada (2015)"},{"key":"9_CR17","unstructured":"Li, W.: Scalable calibration of affinity matrices from incomplete observations. In: Asian Conference on Machine Learning, pp. 753\u2013768. PMLR, Bangkok, Thailand (2020)"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Li, W., Yu, F.: Calibrating distance metrics under uncertainty. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 219\u2013234. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-26409-2_14","DOI":"10.1007\/978-3-031-26409-2_14"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Li, W., Yu, F., Ma, Z.: Metric nearness made practical. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 8648\u20138656 (2023)","DOI":"10.1609\/aaai.v37i7.26041"},{"issue":"2","key":"9_CR20","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","volume":"53","author":"W-C Lin","year":"2020","unstructured":"Lin, W.-C., Tsai, C.-F.: Missing value imputation: a review and analysis of the literature (2006\u20132017). Artif. Intell. Rev. 53(2), 1487\u20131509 (2020). https:\/\/doi.org\/10.1007\/s10462-019-09709-4","journal-title":"Artif. Intell. Rev."},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Optimal neighborhood multiple kernel clustering with adaptive local kernels. IEEE Trans. Knowl. Data Eng. (2020)","DOI":"10.1109\/TKDE.2020.3014104"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Self-representation subspace clustering for incomplete multi-view data. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2726\u20132734 (2021)","DOI":"10.1145\/3474085.3475379"},{"issue":"8","key":"9_CR23","first-page":"2634","volume":"43","author":"X Liu","year":"2020","unstructured":"Liu, X., et al.: Efficient and effective regularized incomplete multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 43(8), 2634\u20132646 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"9_CR24","first-page":"1191","volume":"42","author":"X Liu","year":"2019","unstructured":"Liu, X.: Multiple kernel $$k$$ k-means with incomplete kernels. IEEE Trans. Pattern Anal. Mach. Intell. 42(5), 1191\u20131204 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR25","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.tcs.2018.06.052","volume":"755","author":"R Nader","year":"2019","unstructured":"Nader, R., Bretto, A., Mourad, B., Abbas, H.: On the positive semi-definite property of similarity matrices. Theoret. Comput. Sci. 755, 13\u201328 (2019)","journal-title":"Theoret. Comput. Sci."},{"key":"9_CR26","unstructured":"Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems 14 (2001)"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Nie, F., Tian, L., Li, X.: Multiview clustering via adaptively weighted procrustes. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2022\u20132030 (2018)","DOI":"10.1145\/3219819.3220049"},{"key":"9_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-319-23528-8_20","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"W Shao","year":"2015","unstructured":"Shao, W., He, L., Yu, P.S.: Multiple incomplete views clustering via weighted nonnegative matrix factorization with $$L_{2,1}$$ regularization. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 318\u2013334. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23528-8_20"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Tang, C., et al.: CGD: multi-view clustering via cross-view graph diffusion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5924\u20135931 (2020)","DOI":"10.1609\/aaai.v34i04.6052"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9776\u20139785 (2022)","DOI":"10.1109\/CVPR52688.2022.00955"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Multi-view clustering via late fusion alignment maximization. In: 28th International Joint Conference on Artificial Intelligence, pp. 3778\u20133784 (2019)","DOI":"10.24963\/ijcai.2019\/524"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Xia, R., Pan, Y., Du, L., Yin, J.: Robust multi-view spectral clustering via low-rank and sparse decomposition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8950"},{"issue":"6","key":"9_CR33","doi-asserted-by":"crossref","first-page":"172988141774567","DOI":"10.1177\/1729881417745677","volume":"14","author":"N Xu","year":"2017","unstructured":"Xu, N., Guo, Y., Wang, J., Luo, X., Kong, X.: Multi-view clustering via simultaneously learning shared subspace and affinity matrix. Int. J. Adv. Rob. Syst. 14(6), 1729881417745677 (2017)","journal-title":"Int. J. Adv. Rob. Syst."},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Yu, F., Bao, R., Mao, J., Li, W.: Highly-efficient Robinson-Foulds distance estimation with matrix correction. In: (to appear) 26th European Conference on Artificial Intelligence (2023)","DOI":"10.3233\/FAIA230605"},{"key":"9_CR35","unstructured":"Yu, F., Zeng, Y., Mao, J., Li, W.: Online estimation of similarity matrices with incomplete data. In: Uncertainty in Artificial Intelligence, pp. 2454\u20132464. PMLR (2023)"},{"issue":"3","key":"9_CR36","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/TIP.2018.2877335","volume":"28","author":"K Zhan","year":"2018","unstructured":"Zhan, K., Nie, F., Wang, J., Yang, Y.: Multiview consensus graph clustering. IEEE Trans. Image Process. 28(3), 1261\u20131270 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"9_CR37","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1109\/TCYB.2017.2751646","volume":"48","author":"K Zhan","year":"2017","unstructured":"Zhan, K., Zhang, C., Guan, J., Wang, J.: Graph learning for multiview clustering. IEEE Trans. Cybern. 48(10), 2887\u20132895 (2017)","journal-title":"IEEE Trans. Cybern."},{"issue":"20","key":"9_CR38","doi-asserted-by":"publisher","first-page":"5755","DOI":"10.3390\/s20205755","volume":"20","author":"P Zhang","year":"2020","unstructured":"Zhang, P., et al.: Adaptive weighted graph fusion incomplete multi-view subspace clustering. Sensors 20(20), 5755 (2020)","journal-title":"Sensors"},{"issue":"11","key":"9_CR39","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","volume":"85","author":"S Zhang","year":"2012","unstructured":"Zhang, S.: Nearest neighbor selection for iteratively KNN imputation. J. Syst. Softw. 85(11), 2541\u20132552 (2012)","journal-title":"J. Syst. Softw."},{"key":"9_CR40","unstructured":"Zhao, H., Liu, H., Fu, Y.: Incomplete multi-modal visual data grouping. In: 25th International Joint Conference on Artificial Intelligence, pp. 2392\u20132398 (2016)"},{"issue":"4","key":"9_CR41","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1109\/TNNLS.2019.2919900","volume":"31","author":"S Zhou","year":"2019","unstructured":"Zhou, S., et al.: Multiple kernel clustering with neighbor-kernel subspace segmentation. IEEE Trans. Neural Netw. Learn. Syst. 31(4), 1351\u20131362 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9_CR42","doi-asserted-by":"crossref","unstructured":"Zong, L., Zhang, X., Liu, X., Yu, H.: Weighted multi-view spectral clustering based on spectral perturbation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11625"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8145-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T11:00:29Z","timestamp":1730631629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8145-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981441","9789819981458"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8145-8_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}