Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2013.226
Title: Correntropy induced L2 graph for robust subspace clustering
Authors: Lu, C.
Tang, J.
Lin, M.
Lin, L.
Yan, S. 
Lin, Z.
Issue Date: 2013
Source: 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
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