id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_beo6544t6be6fg7jquetgu7gsu	Carlos A. Loza	RobOMP: Robust variants of Orthogonal Matching Pursuit for sparse representations	2019	24	.pdf	application/pdf	9937	1258	65	RobOMP: Robust variants of Orthogonal Matching Pursuit for sparse representations. of CMP by reformulating the active set update under the lens of robust linear regression; We present three different sets of results to validate the proposed robust, sparse block coordinate descent to separately optimize the sparse code, x, and the weight vector, RobOMP estimate the sparse code with parameter K =10. Table 3 Average norm of sparse code errors of MSE–based OMPs and robust alternatives for different only a robust sparse code estimator, but also a statistically efficient one that exploits the Figure 4 Average normalized norm of sparse code error of MSE–based OMPs and robust alternatives Figure 4 Average normalized norm of sparse code error of MSE–based OMPs and robust alternatives Figure 4 Average normalized norm of sparse code error of MSE–based OMPs and robust alternatives given a dictionary D, then, the optimal sparse code, x̂ij, and estimated denoised image, Ẑ,	./cache/work_beo6544t6be6fg7jquetgu7gsu.pdf	./txt/work_beo6544t6be6fg7jquetgu7gsu.txt