Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2013.226
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dc.titleCorrentropy induced L2 graph for robust subspace clustering
dc.contributor.authorLu, C.
dc.contributor.authorTang, J.
dc.contributor.authorLin, M.
dc.contributor.authorLin, L.
dc.contributor.authorYan, S.
dc.contributor.authorLin, Z.
dc.date.accessioned2014-10-07T04:42:56Z
dc.date.available2014-10-07T04:42:56Z
dc.date.issued2013
dc.identifier.citationLu, C., Tang, J., Lin, M., Lin, L., Yan, S., Lin, Z. (2013). Correntropy induced L2 graph for robust subspace clustering. Proceedings of the IEEE International Conference on Computer Vision : 1801-1808. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2013.226
dc.identifier.isbn9781479928392
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83590
dc.description.abstractIn this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different regularization of the representation coefficient. We instead focus on the robustness of the model to non-Gaussian noises. We propose a new robust clustering method by using the correntropy induced metric, which is robust for handling the non-Gaussian and impulsive noises. Also we further extend the method for handling the data with outlier rows/features. The multiplicative form of half-quadratic optimization is used to optimize the non-convex correntropy objective function of the proposed models. Extensive experiments on face datasets well demonstrate that the proposed methods are more robust to corruptions and occlusions. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICCV.2013.226
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICCV.2013.226
dc.description.sourcetitleProceedings of the IEEE International Conference on Computer Vision
dc.description.page1801-1808
dc.description.codenPICVE
dc.identifier.isiut000351830500225
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