{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:39:21Z","timestamp":1742931561804,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984343"},{"type":"electronic","value":"9789819984350"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"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\/978-981-99-8435-0_19","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"239-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Local Neighbor Propagation Embedding"],"prefix":"10.1007","author":[{"given":"Wenduo","family":"Ma","sequence":"first","affiliation":[]},{"given":"Hengzhi","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Shenglan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yunheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"issue":"6","key":"19_CR1","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_CR2","doi-asserted-by":"publisher","unstructured":"Cai, Y., Mohan, S., Niranjan, A., Jain, N., Cloninger, A., Das, S.: A manifold learning based video prediction approach for deep motion transfer. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW) pp. 4214\u20134221 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00470","DOI":"10.1109\/ICCVW54120.2021.00470"},{"issue":"10","key":"19_CR3","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/LSP.2014.2332118","volume":"21","author":"C Dang","year":"2014","unstructured":"Dang, C., Aghagolzadeh, M., Radha, H.: Image super-resolution via local self-learning manifold approximation. IEEE Signal Process. Lett. 21(10), 1245\u20131249 (2014). https:\/\/doi.org\/10.1109\/LSP.2014.2332118","journal-title":"IEEE Signal Process. Lett."},{"issue":"10","key":"19_CR4","doi-asserted-by":"publisher","first-page":"5591","DOI":"10.1073\/pnas.1031596100","volume":"100","author":"DL Donoho","year":"2003","unstructured":"Donoho, D.L., Grimes, C.: Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. 100(10), 5591\u20135596 (2003)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"8","key":"19_CR5","doi-asserted-by":"publisher","first-page":"2884","DOI":"10.1016\/j.patcog.2012.02.005","volume":"45","author":"J Gui","year":"2012","unstructured":"Gui, J., Sun, Z., Jia, W., Hu, R., Lei, Y., Ji, S.: Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recogn. 45(8), 2884\u20132893 (2012)","journal-title":"Pattern Recogn."},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005) Volume 1, vol. 2, pp. 1208\u20131213. IEEE (2005)","DOI":"10.1109\/ICCV.2005.167"},{"key":"19_CR7","unstructured":"He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems 16 (2003)"},{"key":"19_CR8","unstructured":"Kokiopoulou, E., Saad, Y.: Orthogonal neighborhood preserving projections. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), pp. 8\u2013pp. IEEE (2005)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Kruskal, J.B., Wish, M.: Multidimensional scaling, vol.\u00a011. Sage (1978)","DOI":"10.4135\/9781412985130"},{"issue":"3","key":"19_CR10","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1109\/LSP.2017.2776167","volume":"25","author":"Y Li","year":"2018","unstructured":"Li, Y., Wang, C., Zhao, J.: Locally linear embedded sparse coding for spectral reconstruction from rgb images. IEEE Signal Process. Lett. 25(3), 363\u2013367 (2018). https:\/\/doi.org\/10.1109\/LSP.2017.2776167","journal-title":"IEEE Signal Process. Lett."},{"issue":"5","key":"19_CR11","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1109\/TPAMI.2007.70735","volume":"30","author":"T Lin","year":"2008","unstructured":"Lin, T., Zha, H.: Riemannian manifold learning. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 796\u2013809 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Narayan, A., Berger, B., Cho, H.: Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat. Biotechnol. (2021). https:\/\/doi.org\/10.1038\/s41587-020-00801-7","DOI":"10.1038\/s41587-020-00801-7"},{"issue":"5","key":"19_CR14","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1109\/LSP.2011.2126020","volume":"18","author":"X Nie","year":"2011","unstructured":"Nie, X., Liu, J., Sun, J., Liu, W.: Robust video hashing based on double-layer embedding. IEEE Signal Process. Lett. 18(5), 307\u2013310 (2011). https:\/\/doi.org\/10.1109\/LSP.2011.2126020","journal-title":"IEEE Signal Process. Lett."},{"key":"19_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/11538059_13","volume-title":"Advances in Intelligent Computing","author":"Y Pang","year":"2005","unstructured":"Pang, Y., Zhang, L., Liu, Z., Yu, N., Li, H.: Neighborhood preserving projections (NPP): a novel linear dimension reduction method. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 117\u2013125. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11538059_13"},{"issue":"1","key":"19_CR16","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.patcog.2009.05.005","volume":"43","author":"L Qiao","year":"2010","unstructured":"Qiao, L., Chen, S., Tan, X.: Sparsity preserving projections with applications to face recognition. Pattern Recogn. 43(1), 331\u2013341 (2010)","journal-title":"Pattern Recogn."},{"key":"19_CR17","unstructured":"Roweis, S., Saul, L., Hinton, G.E.: Global coordination of local linear models. In: Advances in Neural Information Processing Systems 14 (2001)"},{"key":"19_CR18","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_CR19","unstructured":"Saul, L.K., Roweis, S.T.: An introduction to locally linear embedding. unpublished. http:\/\/www.cs.toronto.edu\/~roweis\/lle\/publications.html (2000)"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Tenenbaum, J.B., Silva, V.d., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319\u20132323 (2000)","DOI":"10.1126\/science.290.5500.2319"},{"issue":"66\u201371","key":"19_CR21","first-page":"13","volume":"10","author":"L Van Der Maaten","year":"2009","unstructured":"Van Der Maaten, L., Postma, E., Van den Herik, J., et al.: Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10(66\u201371), 13 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR22","unstructured":"Weinberger, K.Q., Saul, L.K.: An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In: AAAI, vol.\u00a06, pp. 1683\u20131686 (2006)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Xiang, S., Nie, F., Pan, C., Zhang, C.: Regression reformulations of lle and ltsa with locally linear transformation. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 41(5), 1250\u20131262 (2011)","DOI":"10.1109\/TSMCB.2011.2123886"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, J.: Mlle: Modified locally linear embedding using multiple weights. In: Advances in Neural Information Processing Systems 19 (2006)","DOI":"10.7551\/mitpress\/7503.003.0204"},{"key":"19_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/978-3-540-45080-1_66","volume-title":"Intelligent Data Engineering and Automated Learning","author":"Z Zhang","year":"2003","unstructured":"Zhang, Z., Zha, H.: Nonlinear dimension reduction via local tangent space alignment. In: Liu, J., Cheung, Y., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 477\u2013481. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45080-1_66"}],"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\/978-981-99-8435-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T19:34:08Z","timestamp":1730921648000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8435-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984343","9789819984350"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8435-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","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":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}