{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:29:28Z","timestamp":1742912968356,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030873578"},{"type":"electronic","value":"9783030873585"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87358-5_17","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:54:11Z","timestamp":1632959651000},"page":"216-225","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Supervised Non-negative Matrix Factorization Induced by Huber Loss"],"prefix":"10.1007","author":[{"given":"Ying","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Sheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binbin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"issue":"6755","key":"17_CR1","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788\u2013791 (1999)","journal-title":"Nature"},{"issue":"6","key":"17_CR2","first-page":"556","volume":"13","author":"DD Lee","year":"2001","unstructured":"Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. Neural Inf. Process. Syst. 13(6), 556\u2013562 (2001)","journal-title":"Neural Inf. Process. Syst."},{"issue":"08","key":"17_CR3","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","volume":"33","author":"D Cai","year":"2011","unstructured":"Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(08), 1548\u20131560 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"17_CR4","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1109\/TIP.2011.2105496","volume":"20","author":"N Guan","year":"2011","unstructured":"Guan, N., Tao, D., Luo, Z., Yuan, B.: Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent. IEEE Trans. Image Process. 20(7), 2030\u20132048 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"He, M., Wei, F., Jia, X.: Globally maximizing, locally minimizing: regularized nonnegative matrix factorization for hyperspectral data feature extraction. In: 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), pp. 1\u20134 (2012)","DOI":"10.1109\/WHISPERS.2012.6874325"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Liu, F.: Dual locality preserving nonnegative matrix factorization for image analysis. In: 2012 IEEE International Conference on Granular Computing, pp. 300\u2013303 (2012)","DOI":"10.1109\/GrC.2012.6468564"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.engappai.2017.11.008","volume":"69","author":"Y Meng","year":"2018","unstructured":"Meng, Y., Shang, R., Jiao, L., Zhang, W., Yang, S.: Dual-graph regularized non-negative matrix factorization with sparse and orthogonal constraints. Eng. Appl. Artif. Intell. 69, 24\u201335 (2018)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.ins.2018.01.008","volume":"435","author":"W Wu","year":"2018","unstructured":"Wu, W., Kwong, S., Zhou, Y., Jia, Y., Gao, W.: Nonnegative matrix factorization with mixed hypergraph regularization for community detection. Inf. Sci. 435, 263\u2013281 (2018)","journal-title":"Inf. Sci."},{"issue":"02","key":"17_CR9","doi-asserted-by":"publisher","first-page":"1940006","DOI":"10.1142\/S021969131940006X","volume":"17","author":"WS Chen","year":"2019","unstructured":"Chen, W.S., Wang, Q., Pan, B., Chen, B.: Nonnegative matrix factorization with manifold structure for face recognition. Int. J. Wavelets Multiresolution Inf. Process. 17(02), 1940006 (2019)","journal-title":"Int. J. Wavelets Multiresolution Inf. Process."},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Du, L., Li, X., Shen, Y.: Robust nonnegative matrix factorization via half-quadratic minimization. In: 2012 IEEE 12th International Conference on Data Mining, pp. 201\u2013210 (2012)","DOI":"10.1109\/ICDM.2012.39"},{"key":"17_CR11","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.3389\/fgene.2019.01054","volume":"10","author":"CY Wang","year":"2019","unstructured":"Wang, C.Y., Liu, J.X., Yu, N., Zheng, C.H.: Sparse graph regularization non-negative matrix factorization based on Huber loss model for cancer data analysis. Front. Genet. 10, 1054 (2019)","journal-title":"Front. Genet."},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Kong, D., Ding, C., Huang, H.: Robust nonnegative matrix factorization using $$l_{2,1}$$-norm. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 673\u2013682 (2011)","DOI":"10.1145\/2063576.2063676"},{"key":"17_CR13","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.neucom.2015.08.126","volume":"204","author":"B Mao","year":"2016","unstructured":"Mao, B., Guan, N., Tao, D., Huang, X., Luo, Z.: Correntropy induced metric based graph regularized non-negative matrix factorization. Neurocomputing 204, 172\u2013182 (2016)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87358-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:59:25Z","timestamp":1632963565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87358-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873578","9783030873585"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87358-5_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2021.csig.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"421","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":"198","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":"47% - 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":"3","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":"3","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)"}},{"value":"Conference was postponed due to the COVID19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}