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|Title:||Correntropy induced L2 graph for robust subspace clustering||Authors:||Lu, C.
|Issue Date:||2013||Citation:||Lu, 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||Abstract:||In 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.||Source Title:||Proceedings of the IEEE International Conference on Computer Vision||URI:||http://scholarbank.nus.edu.sg/handle/10635/83590||ISBN:||9781479928392||DOI:||10.1109/ICCV.2013.226|
|Appears in Collections:||Staff Publications|
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