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The Rate-Distortion Function and Excess-Distortion Exponent of Sparse Regression Codes With Optimal Encoding
This paper studies the performance of sparse regression codes for lossy compression with the squared-error distortion criterion. In a sparse regression code, codewords are linear combinations of subsets of columns of a design matrix. It is shown that with minimum-distance encoding, sparse regression codes achieve the Shannon rate-distortion function for i.i.d. Gaussian sources R * (D) as well as the optimal excess-distortion exponent. This completes a previous result which showed that R * (D)doi:10.17863/cam.10218 fatcat:zksqfnnmhnacdlmt36i5bcge44