[Beck, A. and Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences 2(1): 183-202.10.1137/080716542]Search in Google Scholar
[Bioucas-Dias, J.M. and Figueiredo, M.A. (2007). A new twist: Two-step iterative shrinkage/thresholding algorithms for image restoration, IEEE Transactions on Image Processing 16(12): 2992-3004.10.1109/TIP.2007.909319]Search in Google Scholar
[Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in Machine Learning 3(1): 1-122.10.1561/2200000016]Search in Google Scholar
[Boyd, S. and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press, Cambridge. 10.1017/CBO9780511804441]Search in Google Scholar
[Charles, A.S., Olshausen, B.A. and Rozell, C.J. (2011). Learning sparse codes for hyperspectral imagery, IEEE Journal of Selected Topics in Signal Processing 5(5): 963-978.10.1109/JSTSP.2011.2149497]Open DOISearch in Google Scholar
[Chen, S.S., Donoho, D.L. and Saunders, M.A. (2001). Atomic decomposition by basis pursuit, SIAM Review 43(1): 129-159.10.1137/S003614450037906X]Open DOISearch in Google Scholar
[Donoho, D.L., Tsaig, Y., Drori, I. and Starck, J.L. (2012). Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit, IEEE Transactions on Information Theory 58(2): 1094-1121.10.1109/TIT.2011.2173241]Open DOISearch in Google Scholar
[Du, Q. and Fowler, J.E. (2007). Hyperspectral image compression using JPEG2000 and principal component analysis, IEEE Geoscience and Remote Sensing Letters 4(2): 201-205.10.1109/LGRS.2006.888109]Open DOISearch in Google Scholar
[Fowler, J.E. (2009). Compressive-projection principal component analysis, IEEE Transactions on Image Processing 18(10): 2230-2242.10.1109/TIP.2009.202508919520637]Search in Google Scholar
[Friedlander, M. and Saunders, M. (2012). A dual active-set quadratic programming method for finding sparse least-squares solutions, Online, University of British Columbia, Vancouver, BC, http://web.stanford.edu/group/SOL/software/asp/bpdual.pdf.]Search in Google Scholar
[Gong, P., Zhang, C., Lu, Z., Huang, J. and Ye, J. (2013). A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems, 30th International Conference on Machine Learning (ICML), Atlanta, GA, USA, pp. 37-45.]Search in Google Scholar
[Hou, Y. and Zhang, Y. (2014). Effective hyperspectral image block compressed sensing using three-dimensional wavelet transform, IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada, pp. 2973-2976.]Search in Google Scholar
[Ji, S., Xue, Y. and Carin, L. (2008). Bayesian compressive sensing, IEEE Transactions on Signal Processing 56(6): 2346-2356.10.1109/TSP.2007.914345]Open DOISearch in Google Scholar
[Kim, S.J., Koh, K., Lustig, M., Boyd, S. andGorinevsky, D. (2007). An interior-point method for large-scale-regularized least squares, IEEE Journal of Selected Topics in Signal Processing 1(4): 606-617.10.1109/JSTSP.2007.910971]Search in Google Scholar
[Mairal, J., Bach, F., Ponce, J. and Sapiro, G. (2010). Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research 11: 19-60.]Search in Google Scholar
[Mallat, S.G. and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing 41(12): 3397-3415.10.1109/78.258082]Open DOISearch in Google Scholar
[Needell, D. and Vershynin, R. (2009). Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit, Foundations of Computational Mathematics 9(3): 317-334.10.1007/s10208-008-9031-3]Open DOISearch in Google Scholar
[Nowak, R.D. and Wright, S.J. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of Selected Topics in Signal Processing 1(4): 586-597.10.1109/JSTSP.2007.910281]Search in Google Scholar
[Nowicki, A., Grochowski, M. and Duzinkiewicz, K. (2012). Data-driven models for fault detection using kernel PCA: A water distribution system case study, International Journal of Applied Mathematics and Computer Science 22(4): 939-949, DOI: 10.2478/v10006-012-0070-1.10.2478/v10006-012-0070-1]Open DOISearch in Google Scholar
[Olshausen, B.A. and Field, D.J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by v1?, Vision Research 37(23): 3311-3325.10.1016/S0042-6989(97)00169-7]Open DOISearch in Google Scholar
[Panek, D., Skalski, A., Gajda, J. and Tadeusiewicz, R. (2015). Acoustic analysis assessment in speech pathology detection, International Journal of Applied Mathematics and Computer Science 25(3): 631-643, DOI: 10.1515/amcs-2015-0046.10.1515/amcs-2015-0046]Open DOISearch in Google Scholar
[Parikh, N. and Boyd, S.P. (2014). Proximal algorithms, Foundations and Trends in Optimization 1(3): 127-139. 10.1561/2400000003]Search in Google Scholar
[Penna, B., Tillo, T. and Olmo, G. (2007). Transform coding techniques for lossy hyperspectral data compression, IEEE Transactions on Geoscience and Remote Sensing 45(5): 1408-1421.10.1109/TGRS.2007.894565]Open DOISearch in Google Scholar
[Reed, S.I. and Yu, X. (1990). Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Transactions on Acoustics, Speech, and Signal Processing 38(10): 1760-1770.10.1109/29.60107]Search in Google Scholar
[Tropp, J.A. and Gilbert, A.C. (2007). Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory 53(12): 4655-4666.10.1109/TIT.2007.909108]Open DOISearch in Google Scholar
[Ülkü, İ. and Töreyin, B.U. (2015a). Sparse coding of hyperspectral imagery using online learning, Signal, Video and Image Processing 9(4): 959-966.10.1007/s11760-015-0753-9]Search in Google Scholar
[Ülkü, İ. and Töreyin, B.U. (2015b). Sparse representations for online-learning-based hyperspectral image compression, Applied Optics 54(29): 8625-8631.10.1364/AO.54.00862526479796]Search in Google Scholar
[Wang, J., Kwon, S. and Shim, B. (2012). Generalized orthogonal matching pursuit, IEEE Transactions on Signal Processing 60(12): 6202-6216.10.1109/TSP.2012.2218810]Search in Google Scholar
[Wang, Z., Nasrabadi, N.M. and Huang, T.S. (2014). Spatial-spectral classification of hyperspectral images using discriminative dictionary designed by learning vector quantization, IEEE Transactions on Geoscience and Remote Sensing 52(8): 4808-4822.10.1109/TGRS.2013.2285049]Search in Google Scholar
[Wright, J., Yang, A.Y., Ganesh, A. and Sastry, S.S. (2009). Robust face recognition via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2): 210-227.10.1109/TPAMI.2008.7919110489]Open DOISearch in Google Scholar
[Yang, A.Y., Zhou, Z., Balasubramanian, A.G., Sastry, S.S. and Ma, Y. (2013). Fast-minimization algorithms for robust face recognition, IEEE Transactions on Image Processing 22(8): 3234-3246.10.1109/TIP.2013.226229223674456]Search in Google Scholar
[Yang, J., Peng, Y., Xu, W. and Dai, Q. (2009). Ways to sparse representation: An overview, Science in China F: Information Sciences 52(4): 675-703.10.1007/s11432-009-0045-5]Search in Google Scholar
[Zhang, Z., Xu, Y., Yang, J., Li, X. and Zhang, D. (2015). A survey of sparse representation: Algorithms and applications, IEEE Access 3: 490-530.10.1109/ACCESS.2015.2430359]Search in Google Scholar
[Zuo, W., Meng, D., Zhang, L., X.F. and Zhang, D. (2013). A generalized iterated shrinkage algorithm for non- convex sparse coding, Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, pp. 217-224.10.1109/ICCV.2013.34]Search in Google Scholar