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Cascia, S. Sclaroff, and V. Athitsos, \u201cFast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.4, pp.322-336, 2000.","DOI":"10.1109\/34.845375"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] S. Birchfield, \u201cElliptical head tracking using intensity gradients and color histograms,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.232-237, 1998.","DOI":"10.1109\/CVPR.1998.698614"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] M. Harville, \u201cA framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models,\u201d European Conference on Computer Vision, pp.543-560, 2002.","DOI":"10.1007\/3-540-47977-5_36"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] B. Georgescu, D. Comaniciu, T.X. Han, and X.S. Zhou, \u201cMulti-model component-based tracking using robust information fusion,\u201d International Workshop on Statistical Methods in Video Processing, pp.61-70, 2004.","DOI":"10.1007\/978-3-540-30212-4_6"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] J. Ho, K.-C. Lee, M.-H. Yang, and D. Kriegman, \u201cVisual tracking using learned linear subspaces,\u201d Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition., pp.782-789, 2004.","DOI":"10.1109\/CVPR.2004.1315111"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] D.A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, \u201cIncremental learning for robust visual tracking,\u201d Int. J. Comput. Vision., vol.77, no.1-3, pp.125-141, 2008.","DOI":"10.1007\/s11263-007-0075-7"},{"key":"7","unstructured":"[7] M.J. Black and A.D. Jepson, \u201cEigentracking: Robust matching and tracking of articulated objects using a view-based representation,\u201d Int. J. Comput. Vis., vol.26, no.1, pp.63-84, 1998."},{"key":"8","unstructured":"[8] B. Liu, J. Huang, L. Yang, and C. Kulikowsk, \u201cRobust tracking using local sparse appearance model and k-selection,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1313-1320, 2011."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, \u201cGraph embedding and extensions: a general framework for dimensionality reduction,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.29, no.1, pp.40-51, 2007.","DOI":"10.1109\/TPAMI.2007.250598"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] X. Zhang, W. Hu, S. Maybank, and X. Li, \u201cGraph based discriminative learning for robust and efficient object tracking,\u201d IEEE International Conference on Computer Vision, pp.1-8, 2007.","DOI":"10.1109\/ICCV.2007.4409034"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] H. Qiao, P. Zhang, B. Zhang, and S. Zheng, \u201cLearning an intrinsic-variable preserving manifold for dynamic visual tracking,\u201d IEEE Trans. Syst., Man, Cybern., Part B (Cybernetics), vol.40, no.3, pp.868-880, 2010.","DOI":"10.1109\/TSMCB.2009.2031559"},{"key":"12","unstructured":"[12] D. Cai, X. He, and K. Zhou, \u201cLocality sensitive discriminant analysis,\u201d International Joint Conference on Artificial Intelligence, pp.708-713, 2007."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma, \u201cRobust recovery of subspace structures by low-rank representation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.35, no.1, pp.171-184, 2013.","DOI":"10.1109\/TPAMI.2012.88"},{"key":"14","unstructured":"[14] D. Cai, X. He, J. Han, and T.S. Huang, \u201cGraph regularized nonnegative matrix factorization for data representation,\u201d Trans. Pattern Anal. Mach. Intell., vol.33, no.8, pp.1548-1560, 2011."},{"key":"15","unstructured":"[15] X. Zhu and J. Lafferty, \u201cHarmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning,\u201d International Conference on Machine Learning, pp.1052-1059, 2005."},{"key":"16","unstructured":"[16] F.R. Chung, \u201cSpectral graph theory,\u201d American Mathematical Society, vol.9, no.6, p.212, 1997."},{"key":"17","unstructured":"[17] X. He and P. Niyogi, \u201cLocality preserving projections,\u201d Advances in Neural Information Processing Systems, vol.16, no.1, pp.186-197, 2002."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] S. Li and Y. Fu, \u201cRobust subspace discovery through supervised low-rank constraints.,\u201d SIAM International Conference on Data Mining, pp.163-171, 2014.","DOI":"10.1137\/1.9781611973440.19"},{"key":"19","unstructured":"[19] J. Li, Y. Wu, J. Zhao, and K. Lu, \u201cLow-rank discriminant embedding for multiview learning,\u201d IEEE Trans. Cybern., pp.1-14, 2016."},{"key":"20","unstructured":"[20] Z. Ding, M. Shao, and Y. Fu, \u201cDeep low-rank coding for transfer learning,\u201d Proc. International Joint Conference on Artificial Intelligence, pp.3453-3459, 2015."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] E.J. Cand\u00e8s, X.D. Li, Y. Ma, and J. Wright, \u201cRobust principal component analysis?,\u201d J. ACM (JACM), vol.58, no.3, p.1-37, 2011.","DOI":"10.1145\/1970392.1970395"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] M. Shao, D. Kit, and Y. Fu, \u201cGeneralized transfer subspace learning through low-rank constraint,\u201d Int. J. Comput. Vision., vol.109, no.1-2, pp.74-93, 2014.","DOI":"10.1007\/s11263-014-0696-6"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Z. Ding and Y. Fu, \u201cLow-rank common subspace for multi-view learning,\u201d IEEE International Conference on Data Mining, pp.110-119, 2014.","DOI":"10.1109\/ICDM.2014.29"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, \u201cEigenfaces vs. fisherfaces: Recognition using class specific linear projection,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.19, no.7, pp.711-720, 1997.","DOI":"10.1109\/34.598228"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] J. Zhao, J. Li, and K. Lu, \u201cRobust visual tracking using sparse discriminative graph embedding,\u201d IEICE Trans. Inf. & Syst., vol.E98-D, no.4, pp.938-947, 2015.","DOI":"10.1587\/transinf.2014EDP7419"},{"key":"26","unstructured":"[26] T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, \u201cRobust visual tracking via multi-task sparse learning,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2042-2049, 2012."},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] T. Zhang, S. Liu, N. Ahuja, M.-H. Yang, and B. Ghanem, \u201cRobust visual tracking via consistent low-rank sparse learning,\u201d Int. J. Comput. Vision., vol.111, no.2, pp.171-190, 2015.","DOI":"10.1007\/s11263-014-0738-0"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] K. Lu, Z. Ding, and S. Ge, \u201cLocally connected graph for visual tracking,\u201d Neurocomputing, vol.120, pp.45-53, 2013.","DOI":"10.1016\/j.neucom.2012.08.053"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] D. Gabay and B. Mercier, \u201cA dual algorithm for the solution of nonlinear variational problems via finite element approximation,\u201d Comput. Math. Appl., vol.2, no.1, pp.17-40, 1976.","DOI":"10.1016\/0898-1221(76)90003-1"},{"key":"30","unstructured":"[30] Q. Gu, Z. Li, and J. Han, \u201cJoint feature selection and subspace learning,\u201d International Joint Conference on Artificial Intelligence, pp.1294-1299, 2011."},{"key":"31","unstructured":"[31] Z. Lin, M. Chen, and Y. Ma, \u201cThe augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices,\u201d Tech. Rep., UIUC, 2009."},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] D. Coppersmith and S. Winograd, \u201cMatrix multiplication via arithmetic progressions,\u201d ACM symposium on Theory of computing, pp.1-6, 1987.","DOI":"10.1145\/28395.28396"},{"key":"33","unstructured":"[33] S. Wang, H. Lu, F. Yang, and M.-H. Yang, \u201cSuperpixel tracking,\u201d International Conference on Computer Vision, pp.1323-1330, 2011."},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] J.F. Henriques, R. Caseiro, P. Martins, and J. Batista, \u201cHigh-speed tracking with kernelized correlation filters,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.3, pp.583-596, 2015.","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"35","unstructured":"[35] K. Zhang, L. Zhang, and M.-H. Yang, \u201cReal-time compressive tracking,\u201d European Conference on Computer Vision. Lecture Notes in Computer Science, vol.7574, pp.864-877, 2012."},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] N. Dalal and B. 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