Universal Regularizers For Robust Sparse Coding and Modeling

Preprint English OPEN
Ramirez, Ignacio; Sapiro, Guillermo;
(2010)
  • Subject: Statistics - Machine Learning | Computer Science - Information Theory

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks.... View more
  • References (52)
    52 references, page 1 of 6

    [1] M. Aharon, M. Elad, and A. Bruckstein. The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. SP, 54(11):4311-4322, Nov. 2006.

    [2] A. Barron, J. Rissanen, and B. Yu. The minimum description length principle in coding and modeling. IEEE Trans. IT, 44(6):2743-2760, 1998.

    [3] J. Bernardo and A. Smith. Bayesian Theory. Wiley, 1994.

    [4] A. Bruckstein, D. Donoho, and M. Elad. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 51(1):34-81, Feb. 2009.

    [5] E. J. Cande`s. Compressive sampling. Proc. of the International Congress of Mathematicians, 3, Aug. 2006.

    [6] E. J. Cande`s, M. Wakin, and S. Boyd. Enhancing sparsity by reweighted `1 minimization. J. Fourier Anal. Appl., 14(5):877-905, Dec. 2008.

    [7] R. Chartrand. Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data. In IEEE ISBI, June 2009.

    [8] S. Chen, D. Donoho, and M. Saunders. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1):33-61, 1998.

    [9] R. Coifman and M. Wickenhauser. Entropy-based algorithms for best basis selection. IEEE Trans. IT, 38:713-718, 1992.

    [10] T. Cover and J. Thomas. Elements of information theory. John Wiley and Sons, Inc., 2 edition, 2006.

  • Metrics
Share - Bookmark