References
- S. Ahmadi-Asl and F.P.A. Beik, Iterative algorithms for least-squares solutions of a quaternion matrix equation, J. Appl. Math. Comput. 53(1) (2017), pp. 95–127. doi: 10.1007/s12190-015-0959-6
- S. Ahmadi-Asl and F.P.A. Beik, An efficient iterative algorithm for quaternionic least-squares problems over the generalized η-(anti-)bi-Hermitian matrices, Linear Multilinear Algebra 65(9) (2017), pp. 1743–1769. doi: 10.1080/03081087.2016.1255172
- F.P.A. Beik and S. Ahmadi-Asl, An iterative algorithm for -(anti)-Hermitian least-squares solutions of quaternion matrix equations, Electron. J. Linear Algebra 30 (2015), pp. 372–401. doi: 10.13001/1081-3810.2844
- F.P.A. Beik, S. Ahmadi-Asl and A. Ameri, On the iterative refinement of the solution of ill-conditioned linear system of equations, Int. J. Comput. Math. 95(2) (2018), pp. 427–443. doi: 10.1080/00207160.2017.1290436
- A. Ben-Israel and T.N.E. Greville, Generalized Inverses: Theory and Applications, Springer, New York, 2003.
- Z. Fan, M. Ni, Q. Zhu and E. Liu, Weighted sparse representation for face recognition, Neurocomputing 151(1) (2015), pp. 304–309. doi: 10.1016/j.neucom.2014.09.035
- A.M. Grigoryan and S.S. Agaian, Quaternion and Octonion Color Image Processing with MATLAB [M], SPIE Press, 2018.
- P.C. Hansen, J.G. Nagy and D.P. OLeary, Deblurring Images: Matrices, Spectra, and Filtering, SIAM, Philadelphia, PA, 2006.
- Y. Hu, D. Zhang, J. Ye, X. Li and X. He, Fast and accurate matrix completion via truncated nuclear norm regularization, IEEE Trans. Pattern Anal. Mach. Intell. 35(9) (2013), pp. 2117–2130. doi: 10.1109/TPAMI.2012.271
- C. Lu, H. Min, J. Cui, L. Zhou and Y. Lei, Face recognition via weighted sparse representation, J. Vis. Commun. Image Represent. 24(2) (2013), pp. 111–116. doi: 10.1016/j.jvcir.2012.05.003
- L. Luo, J. Yang, J. Qian and Y. Tai, Nuclear-l1 norm joint regression for face reconstruction and recognition with mixed noise, Pattern Recognit. 48(12) (2015), pp. 3811–3824. doi: 10.1016/j.patcog.2015.06.012
- L. Luo, J. Yang, J. Qian, Y. Tai and G.F. Lu, Robust image regression based on the extended matrix variate power exponential distribution of dependent noise., IEEE Trans. Neural Netw. Learn. Syst. 28 (2016), pp. 1–15.
- I. Naseem, R. Togneri and M. Bennamoun, Linear regression for face recognition, IEEE Trans. Pattern Anal. Mach. Intell. 32(11) (2010), pp. 2106–2112. doi: 10.1109/TPAMI.2010.128
- J. Qian, L. Luo, J. Yang, F. Zhang and Z. Lin, Robust nuclear norm regularized regression for face recognition with occlusion, Pattern Recognit. 48(10) (2015), pp. 3145–3159. doi: 10.1016/j.patcog.2015.04.017
- J.C. Xie, J. Yang, J.J. Qian and L. Luo, Bi-weighted robust matrix regression for face recognition, Neuro-computing 237 (2017), pp. 375–387.
- J. Yang, L. Luo, J. Qian, Y. Tai, F. Zhang and Y. Gao, Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes, IEEE Trans. Pattern Anal. Mach. Intell 39(1) (2017), pp. 156–171. doi: 10.1109/TPAMI.2016.2535218
- Y.B Yu, N. Peng and J.Y. Gan, Concave convex norm ratio prior based double model and fast algorithm for blind deconvolution, Neurocomputing 171(1) (2016), pp. 781–787.
- S.F. Yuan and A.P. Liao, Least squares Hermitian solution of the complex matrix equation AXB + CXD = E with the least norm, J. Franki. Inst. 351 (2014), pp. 4978–4997. doi: 10.1016/j.jfranklin.2014.08.003
- L. Zhang, M. Yang and X.C. Feng, Sparse representation or collaborative representation: Which helps face recognition? IEEE International Conference on Computer Vision, 2011, pp. 471–478.
- J. Zhang, J. Yang, J. Qian and J. Xu, Nearest orthogonal matrix representation for face recognition, Neurocomputing 151 (2015), pp. 471–480. doi: 10.1016/j.neucom.2014.09.019