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EXTENDED CYCLIC COORDINATE DESCENT FOR ROBUST ROW-SPARSE SIGNAL RECONSTRUCTION IN THE PRESENCE OF OUTLIERS

Citation Author(s):
Huiping Huang, Hing Cheung So, Abdelhak M. Zoubir
Submitted by:
huiping huang
Last updated:
14 May 2020 - 3:52am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Huiping Huang
Paper Code:
SAM-P3.7

Abstract 

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.

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ICASSP2020_Presentation_Huiping_So_Zoubir_Huiping.pdf

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