{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T14:44:30Z","timestamp":1787323470695,"version":"build-2736575974"},"reference-count":42,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"2","funder":[{"DOI":"10.13039\/501100006489","name":"Commissariat \u00e0 l'\u00canergie Atomique et aux \u00canergies Alternatives","doi-asserted-by":"publisher","award":["665044"],"award-info":[{"award-number":["665044"]}],"id":[{"id":"10.13039\/501100006489","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM J. Imaging Sci."],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:p>Supervised dictionary learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semisupervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semisupervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using locally linear embedding, which can be considered a regularization of sparse code; on the other hand, we train a semisupervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semisupervised dictionary learning methods.<\/jats:p>","DOI":"10.1137\/19m1285469","type":"journal-article","created":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T12:48:53Z","timestamp":1588250933000},"page":"724-745","source":"Crossref","is-referenced-by-count":5,"title":["Semisupervised Dictionary Learning with Graph Regularized and Active Points"],"prefix":"10.1137","volume":"13","author":[{"given":"K. H.","family":"Tran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F. M.","family":"Ngol\u00e8 Mboula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. L.","family":"Starck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Prost","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.881199"},{"key":"atypb2","first-page":"192","author":"Babagholami-Mohamadabadi B.","year":"2013","journal-title":"Berlin"},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2183129"},{"key":"atypb4","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1051\/0004-6361\/201220752"},{"key":"atypb6","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-016-0073-1"},{"key":"atypb7","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2602809"},{"key":"atypb8","first-page":"895","author":"Elad M.","year":"2006","journal-title":"IEEE"},{"key":"atypb9","unstructured":"M. J. Gangeh, A. K. Farahat, A. Ghodsi, and M. S. Kamel,\n                      Supervised dictionary learning and sparse representation-a review\n                      , preprint, arXiv:1502.05928, 2015."},{"key":"atypb10","unstructured":"X. He,\n                      Locality Preserving Projections\n                      , PhD thesis, Chicago, IL, 2005. AAI3195015."},{"key":"atypb11","doi-asserted-by":"crossref","unstructured":"P. Irofti and A. B\u0103ltoiu,\n                      Malware identification with dictionary learning\n                      , in 2019 27th European Signal Processing Conference (EUSIPCO), 2019, pp. 1-5,https:\/\/doi.org\/10.23919\/EUSIPCO.2019.8903043.","DOI":"10.23919\/EUSIPCO.2019.8903043"},{"key":"atypb12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.88"},{"key":"atypb13","first-page":"186","author":"Kong S.","year":"2012","journal-title":"Berlin"},{"key":"atypb14","unstructured":"Y. LeCun and C. Cortes,\n                      MNIST handwritten digit database\n                      , 2010,http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"atypb15","doi-asserted-by":"crossref","unstructured":"H. Lee, A. Battle, R. Raina, and A. Y. Ng,\n                      Efficient sparse coding algorithms\n                      , in Advances in Neural Information Processing Systems 19, B. Sch\u00f6lkopf, J. Platt, and T. Hoffman, eds., MIT Press, Cambridge, MA, 2007.","DOI":"10.7551\/mitpress\/7503.003.0105"},{"key":"atypb16","doi-asserted-by":"crossref","unstructured":"J. Mairal, F. Bach, J. Ponce, and G. 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Barner,\n                      Label consistent recursive least squares dictionary learning for image classification\n                      , in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 1888-1892,https:\/\/doi.org\/10.1109\/ICIP.2016.7532686.","DOI":"10.1109\/ICIP.2016.7532686"},{"key":"atypb22","unstructured":"F. M. N. Mboula, J. Starck, S. Ronayette, K. Okumura, and J. Amiaux,\n                      Super-resolution method using sparse regularization for point-spread function recovery\n                      , CoRR, abs\/1410.7679 (2014),http:\/\/arxiv.org\/abs\/1410.7679."},{"key":"atypb23","doi-asserted-by":"crossref","unstructured":"D. Pham and S. Venkatesh,\n                      Joint learning and dictionary construction for pattern recognition\n                      , in 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8,https:\/\/doi.org\/10.1109\/CVPR.2008.4587408.","DOI":"10.1109\/CVPR.2008.4587408"},{"key":"atypb24","unstructured":"A. Rasmus, H. Valpola, M. Honkala, M. Berglund, and T. Raiko,\n                      Semi-supervised learning with ladder networks\n                      , 2015,https:\/\/arxiv.org\/abs\/1507.02672."},{"key":"atypb25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2090538"},{"key":"atypb26","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"atypb27","doi-asserted-by":"crossref","unstructured":"A. Shrivastava, J. K. Pillai, V. M. Patel, and R. 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Ye,\n                      Semi-supervised dictionary learning via structural sparse preserving\n                      , in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, AAAI Press, 2016, pp. 2137-2144.","DOI":"10.1609\/aaai.v30i1.10225"},{"key":"atypb31","doi-asserted-by":"crossref","unstructured":"H. Wang, F. Nie, W. Cai, and H. Huang,\n                      Semi-supervised robust dictionary learning via efficient l-norms minimization\n                      , in 2013 IEEE International Conference on Computer Vision, 2013, pp. 1145-1152,https:\/\/doi.org\/10.1109\/ICCV.2013.146.","DOI":"10.1109\/ICCV.2013.146"},{"key":"atypb32","doi-asserted-by":"crossref","unstructured":"X. Wang, X. Guo, and S. Z. Li,\n                      Adaptively unified semi-supervised dictionary learning with active points\n                      , in 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1787-1795,https:\/\/doi.org\/10.1109\/ICCV.2015.208.","DOI":"10.1109\/ICCV.2015.208"},{"key":"atypb33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"atypb34","unstructured":"Yale,\n                      The extended yale face database b\n                      , (2001),http:\/\/vision.ucsd.edu\/~iskwak\/ExtYaleDatabase\/ExtYaleB.html."},{"key":"atypb35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2050625"},{"key":"atypb36","doi-asserted-by":"crossref","unstructured":"M. Yang, L. Zhang, J. Yang, and D. 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Gao,\n                      Group-based sparse representation for image restoration\n                      , CoRR,http:\/\/arxiv.org\/abs\/1405.3351, 2014.","DOI":"10.1109\/TIP.2014.2323127"},{"key":"atypb41","doi-asserted-by":"crossref","unstructured":"Q. Zhang and B. Li,\n                      Discriminative k-svd for dictionary learning in face recognition\n                      , in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2691-2698,https:\/\/doi.org\/10.1109\/CVPR.2010.5539989.","DOI":"10.1109\/CVPR.2010.5539989"},{"key":"atypb42","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2090535"}],"container-title":["SIAM Journal on Imaging Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/19M1285469","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T14:04:23Z","timestamp":1787321063000},"score":1,"resource":{"primary":{"URL":"https:\/\/epubs.siam.org\/doi\/10.1137\/19M1285469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["10.1137\/19M1285469"],"URL":"https:\/\/doi.org\/10.1137\/19m1285469","relation":{},"ISSN":["1936-4954"],"issn-type":[{"value":"1936-4954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1]]}}}