TY - GEN
T1 - Improving Graph Trend Filtering with Non-convex Penalties
AU - Varma, Rohan
AU - Lee, Harlin
AU - Chi, Yuejie
AU - Kovacevic, Jelena
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper, we study the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of nonconvex regularizers that exhibit superior recovery performance over existing convex ones. We present theoretical results in the form of asymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposed optimization problem and analyze its convergence. Numerical performance of the proposed framework with non-convex regularizers on both synthetic and real-world data are presented for denoising, support recovery, and semi-supervised classification.
AB - In this paper, we study the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of nonconvex regularizers that exhibit superior recovery performance over existing convex ones. We present theoretical results in the form of asymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposed optimization problem and analyze its convergence. Numerical performance of the proposed framework with non-convex regularizers on both synthetic and real-world data are presented for denoising, support recovery, and semi-supervised classification.
KW - graph signal processing
KW - graph trend filtering
KW - nonconvex penalties
KW - piecewise smooth graph signals
KW - semi-supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85068969031&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068969031&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8683279
DO - 10.1109/ICASSP.2019.8683279
M3 - Conference contribution
AN - SCOPUS:85068969031
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5391
EP - 5395
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -