Extended Cyclic Coordinate Descent for Robust Row-Sparse Signal Reconstruction in the Presence of Outliers
Huiping Huang, Hing Cheung So, Abdelhak M. Zoubir
Abstract
The problem of row-sparse signal reconstruction for complex-valued data with outliers is investigated in this paper. First, we formulate the problem by taking advantage of a sparse weight matrix, which is used to down-weight the outliers. The formulated problem belongs to LASSO-type problems, and such problems can be efficiently solved via cyclic coordinate descent (CCD). We propose an extended CCD algorithm to solve the problem for complex-valued measurements, which requires careful characterization and derivation. Numerical simulation results show that the proposed algorithm is robust against outliers and has a higher empirical probability of exact recovery compared with other tested methods.
BibTeX
@inproceedings{icassp2020_extendedcyclicco,
title = {Extended Cyclic Coordinate Descent for Robust Row-Sparse Signal Reconstruction in the Presence of Outliers},
author = {Huiping Huang and Hing Cheung So and Abdelhak M. Zoubir},
booktitle = {ICASSP 2020},
year = {2020}
}