{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:58:06Z","timestamp":1769918286404,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031319747","type":"print"},{"value":"9783031319754","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-31975-4_19","type":"book-chapter","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T23:30:02Z","timestamp":1683761402000},"page":"250-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Graph Laplacian for\u00a0Semi-supervised Learning"],"prefix":"10.1007","author":[{"given":"Or","family":"Streicher","sequence":"first","affiliation":[]},{"given":"Guy","family":"Gilboa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108274","volume":"122","author":"AI Aviles-Rivero","year":"2022","unstructured":"Aviles-Rivero, A.I., Sellars, P., Sch\u00f6nlieb, C.B., Papadakis, N.: Graphxcovid: explainable deep graph diffusion pseudo-labelling for identifying COVID-19 on chest x-rays. Pattern Recogn. 122, 108274 (2022)","journal-title":"Pattern Recogn."},{"issue":"6","key":"19_CR2","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373\u20131396 (2003)","journal-title":"Neural Comput."},{"key":"19_CR3","unstructured":"Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(11) (2006)"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Bresson, X., Laurent, T., Uminsky, D., Von Brecht, J.H.: Multiclass total variation clustering. arXiv preprint arXiv:1306.1185 (2013)","DOI":"10.21236\/ADA612811"},{"issue":"1","key":"19_CR5","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1088\/1361-6544\/aae949","volume":"32","author":"J Calder","year":"2018","unstructured":"Calder, J.: The game theoretic p-laplacian and semi-supervised learning with few labels. Nonlinearity 32(1), 301 (2018)","journal-title":"Nonlinearity"},{"key":"19_CR6","unstructured":"Chen, Z., Li, Y., Cheng, X.: Specnet2: orthogonalization-free spectral embedding by neural networks. arXiv preprint arXiv:2206.06644 (2022)"},{"issue":"1","key":"19_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.acha.2006.04.006","volume":"21","author":"RR Coifman","year":"2006","unstructured":"Coifman, R.R., Lafon, S.: Diffusion maps. Appl. Comput. Harmon. Anal. 21(1), 5\u201330 (2006)","journal-title":"Appl. Comput. Harmon. Anal."},{"issue":"6","key":"19_CR8","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1017\/S0956792517000122","volume":"28","author":"A Elmoataz","year":"2017","unstructured":"Elmoataz, A., Desquesnes, X., Toutain, M.: On the game p-laplacian on weighted graphs with applications in image processing and data clustering. Eur. J. Appl. Math. 28(6), 922\u2013948 (2017)","journal-title":"Eur. J. Appl. Math."},{"issue":"8","key":"19_CR9","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1109\/TPAMI.2014.2300478","volume":"36","author":"C Garcia-Cardona","year":"2014","unstructured":"Garcia-Cardona, C., Merkurjev, E., Bertozzi, A.L., Flenner, A., Percus, A.G.: Multiclass data segmentation using diffuse interface methods on graphs. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1600\u20131613 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735\u20131742. IEEE (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"19_CR11","unstructured":"Hearty, J.: Advanced Machine Learning with Python. Packt Publishing (2016)"},{"key":"19_CR12","unstructured":"Joachims, T.: Transductive learning via spectral graph partitioning. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 290\u2013297 (2003)"},{"key":"19_CR13","unstructured":"Liu, W., He, J., Chang, S.F.: Large graph construction for scalable semi-supervised learning. In: ICML (2010)"},{"key":"19_CR14","unstructured":"Mao, Q., Tsang, I.W.: Parameter-free spectral kernel learning. arXiv preprint arXiv:1203.3495 (2012)"},{"issue":"1","key":"19_CR15","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1137\/0105003","volume":"5","author":"J Munkres","year":"1957","unstructured":"Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32\u201338 (1957)","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"19_CR16","unstructured":"Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14 (2001)"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323\u20132326 (2000)","DOI":"10.1126\/science.290.5500.2323"},{"key":"19_CR18","unstructured":"Shaham, U., Stanton, K., Li, H., Nadler, B., Basri, R., Kluger, Y.: Spectralnet: spectral clustering using deep neural networks. In: Proceedings of the 6th International Conference on Learning Representations (2018)"},{"issue":"8","key":"19_CR19","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."},{"issue":"2","key":"19_CR20","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1007\/s10915-017-0421-z","volume":"73","author":"Z Shi","year":"2017","unstructured":"Shi, Z., Osher, S., Zhu, W.: Weighted nonlocal laplacian on interpolation from sparse data. J. Sci. Comput. 73(2), 1164\u20131177 (2017)","journal-title":"J. Sci. Comput."},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Streicher, O., Cohen, I., Gilboa, G.: Basis: batch aligned spectral embedding space. arXiv preprint arXiv:2211.16960 (2022)","DOI":"10.1109\/CVPR52729.2023.01002"},{"key":"19_CR22","unstructured":"Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, vol. 17 (2004)"}],"container-title":["Lecture Notes in Computer Science","Scale Space and Variational Methods in Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31975-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T04:59:20Z","timestamp":1729400360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31975-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031319747","9783031319754"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31975-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Scale Space and Variational Methods in Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santa Margherita di Pula","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"21 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scalespace2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eventi.unibo.it\/ssvm2023","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":"72","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":"57","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":"79% - 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":"2","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)"}}]}}