{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T22:43:21Z","timestamp":1760395401553,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030606381"},{"type":"electronic","value":"9783030606398"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-60639-8_14","type":"book-chapter","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T10:04:02Z","timestamp":1602669842000},"page":"164-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Depth-Adaptive Discriminant Projection with Optimal Transport"],"prefix":"10.1007","author":[{"given":"Peng","family":"Wan","sequence":"first","affiliation":[]},{"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"14_CR1","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems (NIPS 2013), Lake Tahoe, Nevada, USA, pp. 2292\u20132300 (2013)"},{"key":"14_CR2","unstructured":"Dorfer, M., Kelz, R., Widmer, G.: Deep linear discriminant analysis. In: Proceedings of the International Conference on Learning Representations (ICLR 2015), pp. 1\u201313 (2015)"},{"issue":"3","key":"14_CR3","doi-asserted-by":"publisher","first-page":"675","DOI":"10.3233\/IDA-173365","volume":"22","author":"Y Du","year":"2018","unstructured":"Du, Y., Lu, X., Zeng, W., Hu, C.: A novel fuzzy linear discriminant analysis for face recognition. Intell. Data Anal. 22(3), 675\u2013696 (2018)","journal-title":"Intell. Data Anal."},{"key":"14_CR4","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.neucom.2018.02.064","volume":"291","author":"M Juuti","year":"2018","unstructured":"Juuti, M., Corona, F., Karhunen, J.: Stochastic discriminant analysis for linear supervised dimension reduction. Neurocomputing 291, 136\u2013150 (2018)","journal-title":"Neurocomputing"},{"issue":"1","key":"14_CR5","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TCSVT.2003.818352","volume":"14","author":"Q Liu","year":"2004","unstructured":"Liu, Q., Lu, H., Ma, S.: Improving kernel fisher discriminant analysis for face recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 42\u201349 (2004)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"4","key":"14_CR6","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1109\/TNNLS.2012.2183645","volume":"23","author":"A Stuhlsatz","year":"2012","unstructured":"Stuhlsatz, A., Lippel, J., Zielke, T.: Feature extraction with deep neural networks by a generalized discriminant analysis. IEEE Trans. Neural Netw. Learn. Syst. 23(4), 596\u2013608 (2012)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"14_CR7","first-page":"1198","volume":"33","author":"M Wang","year":"2019","unstructured":"Wang, M., Huang, J., Liu, M., Zhang, D.: Functional connectivity network analysis with discriminative hub detection for brain disease identification. Assoc. Adv. Artif. Intell. (AAAI) 33, 1198\u20131205 (2019)","journal-title":"Assoc. Adv. Artif. Intell. (AAAI)"},{"key":"14_CR8","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.patcog.2016.12.022","volume":"65","author":"L Wu","year":"2017","unstructured":"Wu, L., Shen, C., van den Hengel, A.: Deep linear discriminant analysis on fisher networks: a hybrid architecture for person re-identification. Pattern Recogn. 65, 238\u2013250 (2017)","journal-title":"Pattern Recogn."},{"issue":"2","key":"14_CR9","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TPAMI.2005.33","volume":"27","author":"J Yang","year":"2005","unstructured":"Yang, J., Frangi, A.F., Yang, J., Zhang, D., Jin, Z.: KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 230\u2013244 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1908","DOI":"10.1109\/TPAMI.2015.2497686","volume":"38","author":"M Yu","year":"2016","unstructured":"Yu, M., Shao, L., Zhen, X., He, X.: Local feature discriminant projection. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1908\u20131914 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, 19\u201325 August 2017, pp. 3553\u20133559 (2017)","DOI":"10.24963\/ijcai.2017\/497"}],"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\/978-3-030-60639-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T22:03:41Z","timestamp":1760393021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60639-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030606381","9783030606398"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60639-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 October 2020","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":"Nanjing","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/index_en.html","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":"Microsoft CMT system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"402","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":"158","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":"39% - 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":"4","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)"}}]}}