By Sohail Bahmani
This thesis demonstrates options that offer swifter and extra actual ideas to various difficulties in desktop studying and sign processing. the writer proposes a "greedy" set of rules, deriving sparse suggestions with promises of optimality. using this set of rules gets rid of the various inaccuracies that happened with using past models.
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Blumensath and M. E. Davies. Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis, 27(3):265–274, Nov. 2009. P. Boufounos and R. Baraniuk. 1-bit compressive sensing. In Information Sciences and Systems, 2008. CISS 2008. 42nd Annual Conference on, pages 16–21, Mar. 2008. E. J. Candès. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9–10):589–592, 2008. E. J. Candès and X. Li. Solving quadratic equations via PhaseLift when there are about as many equations as unknowns.
In these scenarios the logistic loss is merely convex and it does not have a unique minimum. Furthermore, it is possible, especially in underdetermined problems, that the observed data is linearly separable. In that case one can achieve arbitrarily small loss values by tending the parameters to infinity along certain directions. To compensate for these drawbacks the logistic loss is usually regularized by some penalty term Hastie et al. (2009); Bunea (2008). , kxk22 ). x/ C kxk22 : 2 For any convex g .
Rezaiifar, and P. S. Krishnaprasad. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Conference Record of the 27th Asilomar Conference on Signals, Systems and Computers, volume 1, pages 40–44, Pacific Grove, CA, Nov. 1993. Y. Shechtman, Y. C. Eldar, A. Szameit, and M. Segev. Sparsity based sub-wavelength imaging with partially incoherent light via quadratic compressed sensing. Optics Express, 19(16):14807–14822, July 2011a. Y. Shechtman, A.
Algorithms for Sparsity-Constrained Optimization by Sohail Bahmani