Robust overcomplete matrix recovery for sparse sources using a generalized Hough transform
نویسندگان
چکیده
We propose an algorithm for recovering the matrix A in X = AS where X is a random vector of lower dimension than S. S is assumed to be sparse in the sense that S has less nonzero elements than the dimension of X at any given time instant. In contrast to previous approaches, the computational time of the presented algorithm is linear in the sample number and independent of source dimension, and the algorithm is robust against noise. Experiments confirm these theoretical results.
منابع مشابه
Robust Sparse Component Analysis Based on a Generalized Hough Transform
1 Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany 2ECECS Department and Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221, USA 3BSI RIKEN, Laboratory for Advanced Brain Signal Processing, 2-1, Hirosawa, Wako, Saitama 351-0198, Japan 4Faculty of Electrical Engineering, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsa...
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