|Commenced in January 2007||Frequency: Monthly||Edition: International||Publications Count: 29530|
 B. Olshausen and D. Field, “Emergence of simple-cell receptive fieldproperties by learning a sparse code for natural images,” Nature, vol.381, pp. 607–609, 1996.
 M. S. Lewicki and T. J. Sejnowski, “Learning overcomplete representations,” Neural Comput., vol. 12, pp. 337–365, 2000.
 P. Schmid-Saugeon and A. Zakhor, “Dictionary design for matchingpursuit and application to motion-compensated video coding,” IEEETrans. Circuits Syst. Video Technol., vol. 14, no. 6, pp. 880–886, 2004.
 M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEETrans. Signal Process, vol. 54, no. 11, pp. 4311–4322, 2006.
 M. Yaghoobi, L. Daudet, and M. Davies, “Parametric dictionary design for sparse coding,” in Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS09), 2009
 M. G. Jafari and M. D. Plumbley. Fast dictionary learning for sparserepresentations of speech signals. IEEE Journal of Selected Topics in SignalProcessing, 5:1025–1031, Sep. 2011.
 N. E. Huang, Z. Shen, S. R. Long, M. C. Wu. H.H. Shih, et al, “The Empirical Mode Decompositionand The Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis”, Proc. R. Soc. A,1998, 454, pp. 903-995.
 A. Gersho and R. M. Gray, Vector Quantization and Signal Compression.Norwell, MA: Kluwer Academic, 1991.
 T. Blumensath and M. E. Davies “Iterative thresholding for sparse approximations", J. Fourier Anal. Applicat., vol. 14, no. 5, pp.629 -654 2008