[1] G. Liu, Z. Liu and Y. Yu, Robust subspace segmentation
by low-rank representation, Proceedings of ICML,2010.
[2] G. Liu, Z. Liu, J. Sun, Y. Yu and Y. Ma, Robust recovery
of subspace structures by low-rank representation,
Pattern Analysis and Machine Intelligence, IEEE
Transaction on, 35 (2013), pp. 171–184.
[3] J. Wright, A. Ganesh, S. Rao, Y. Peng and Y. Ma,
Robust principal component analysis: Exact recovery
of corrupted low-rank matrices via convex optimization,
Proceedings of NIPS,2009.
[4] C. Lang, G. Liu, J. Yu and S. Yan, Saliency detection
by multitask sparsity pursuit, Image Processing, IEEE
Transaction on, 21 (2012), pp. 1327–1338.
[5] F. Porikli and O. Tuzel, Tuzel: Covariance tracker,
Proceedings of CVPR, 2006.
[6] B. Ma, Y. Su, F. Jurie, et al, Bicov: a novel image representation
for person re-identification and face verification,
Proceedings of BMVC, 2012.
[7] Y. Xie, J. Ho and B. Vemuri, On a nonlinear generalization
of sparse coding and dictionary learning, Proceedings
of ICML, 2013.
[8] O. Tuzel, F. Porikli and P. Meer, Region covariance:
a fast descriptor for detection and classification, Proceedings
of ECCV, 2006.
[9] W. Ziller, Examples of Riemannian manifolds with
non-negative sectional curvature, Metric and Comparison
Geometry, Surv. Diff. Gem, International Press,
(11) 2007, pp. 63–102.
[10] S. Sra and A. Cherian, Generalized dictionary learning
for symmetric postive definite matrices with application
to nearest neigbour retrieval, Proceedings of ECML,
Springer, 2011.
[11] V. Arsigny, P. Fillard, X. Pennec and N. Ayache,
Log-Euclidean metrics for fast and simple calculus on
diffusion tensors, Magnetic Resonance in Medicine,
56(2)(2006), pp. 411-421.
[12] M. T. Harandi, C. Sanderson, R. Hartley and B. C.
Lovell, Sparse coding and dictionary learning for symmetric
positive definite matrices: a kernel approach,
Proceedings of ECCV, Springer, 2012.
[13] S. Jayasumana, R. Hartley, M. Salzmann, H. Li and M.
Harandi, Kernel methods on the Riemannian manifold
of symmetric positive definite matrices, Proceedings of
CVPR, 2013.
[14] P. Li, Q. Wang, W. Zuo and L. Zhang, Log-Euclidean
kernels for sparse representation and dictionary learning,
Proceedings of ICCV, 2013.
[15] A. Cherian and S. Sra, Riemannian sparse coding for
positive definite matrices, Proceedings of ECCV, 2014.
[16] B. Wang, Y. Hu, J. Gao, Y. Sun and B. Yin Low
rank representation on Grassmann manifold, accepted
by ACCV, 2014.
[17] Y. Pang, Y. Yuan and X. Li, Gabor-based region
covariance matrices for face recognition, Circuits and
Systems for Video Technology, IEEE Transaction on,
18(7)(2008), pp. 989–993.
[18] S. Amari and H. Nagaoka, Methods of Information
Geometry, American Mathematical Society, 2007.
[19] Y. Shen, Z. Wen and Y. Zhang, Augmented Lagrangian
alternating direction method for matrix separation
based on low-rank factorization, Optimization
Methods and Software, 29(2014), pp. 239–263.
[20] C. C. Chang and C. J. Lin, LIBSVM: A library
for support vector machines, ACM Transactions on
Intelligent Systems and Technology, 2(2011), PP. 27:1–
27:27.
[21] J. Shi and J. Malik, Normalized cuts and image segmentation,
Pattern Analysis and Machine Intelligence,
IEEE Transaction on, 22(2000), pp. 888–905.
[22] P. Brodatz, Textures: a photographic album for artists
and designers, vol. 66, Dover New York, 1966.
[23] D. Hoiem, A. A. Efros and M. Hebert Automatic photo
pop-up, Proceedings of SIGGRAPH, 2005, pp. 577–584.
[24] J. M. Odobez, Idiap head pose database,
http://www.idiap.ch/dataset/headpose.
[25] S. Ba and J. Odobez, Evaluation of multiple cue head
pose estimation algorithms in natural environements,
Proceedings of ICME, 2005, pp. 1330-1333.
[26] J. Liu, Y. Chen J. Zhang and Z. Xu, Enhancing lowrank
subspace clustering by manifold regularization,
Image Processing , IEEE Transaction on, 23(9)(2014),
pp. 4022–4030.
[27] Z. Zhang and K. Zhao, Low-rank matrix approximation
with manifold regularization, Pattern Analysis and Machine
Intelligence, IEEE Transaction on, 35(7)(2013),
pp. 1717–1729.
[28] U. Shalit, D. Weinshall and G. Chechik, Online learning
in the embedded manifold of low-rank matrices.
Journal of Machine Learning Research, 13(2012), pp.
429–458.
[29] Y. C. Eldar, D. Needell and Y. Plan, Uniqueness
conditions for low-rank matrix recovery, Proceedings
of SPIE, 2011.
[30] B. Vandereycken, P. A. Asil and S. Vandewalle, A
Riemannian geometry with complete geodesics for the
set of positive semidefinite matrices of fixed rank, IMA
Journal of Numerical Analysis, 33(2013), pp. 481–514.
[31] J. Cai, E. J. Candes and Z. Shen, A singular value
thresholding algorithm for matrix completion, SIAM J.
on Optimization, 20(4)(2010), pp. 1956–1982.
[32] B. Ma, Y. Wu, F. Sun, Affine object tracking using
kernel-based region covariance descriptors, Foundations
of Intelligent Systems, Springer, (2012), pp. 613–
623.
[33] R. Sivalingam, D. Boley, V. Morellas and N. Papanikolopoulos,
Tensor sparse coding for region covariances,
Proceedings of ECCV, Springer, 2010.
[34] J. M. Lee, Introduction to Smooth Manifolds, Graduate
Texts in Mathematics, 218(2002), Spring.
[35] Z. Lin, R. Liu and Z. Su, Linearized alternating direction
method with adaptive penalty for low rank representation,
Proceedings of NIPS, 2011.
[36] G. H. Chen, B. F. Wang and C. J. Lu, On the
Parallel Computation of the Algebraic Path Problem,
Parallel and Distributed Systems, IEEE Transactions
on, 3(2)(1992), pp. 251–256.
[37] J. Gui, D. Tao, Z. Sun, Y. Luo, X. You and Y.
Tang, Group sparse multiview patch alignment framework
with view consistency for image classification, Image
Processing, IEEE Transactions on, 23(7) (2014),
pp. 3126–3137.
[38] J. Gui, Z. Sun, J. Cheng, S. Ji and X. Wu, How to estimate
the regularization parameter for spectral regression
discriminant analysis and its kernel version?, Circuits
and Systems for Video Technology, IEEE Transactions
on, 24(2)(2014), pp. 211–223.
[39] B. Geng, D. Tao, C. Xu, L. Yang and X. Hua, Ensemble
manifold regularization, Pattern Analysis and Machine
Intelligence, IEEE Transactions on, 3(6)(2012),
pp. 1227–1233.