TY - GEN
T1 - The Suppression of Transient Artifacts in Time Series via Convex Analysis
AU - Feng, Yining
AU - Graber, Harry
AU - Selesnick, Ivan
N1 - Funding Information:
VIII. ACKNOWLEDGMENT The authors thank Randall Barbour for important discussions. This work was supported by NSF (grant CCF-1525398).
Funding Information:
The authors thank Randall Barbour for important discussions. This work was supported by NSF (grant CCF-1525398).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - For the suppression of transient artifacts in time series data, we propose a non-convex generalized fused lasso penalty for the estimation of signals comprising a low-pass signal, a sparse piecewise constant signal, and additive white Gaussian noise. The proposed non-convex penalty is designed so as to preserve the convexity of the total cost function to be minimized, thereby realizing the benefits of a convex optimization framework (reliable, robust algorithms, etc Compared to the conventional use of L1 norm penalty, the proposed non-convex penalty does not underestimate the true amplitude of signal values. We derive a fast proximal algorithm to implement the method. The proposed method is demonstrated on the suppression of artifacts in near infrared spectroscopic (NIRS) measures.
AB - For the suppression of transient artifacts in time series data, we propose a non-convex generalized fused lasso penalty for the estimation of signals comprising a low-pass signal, a sparse piecewise constant signal, and additive white Gaussian noise. The proposed non-convex penalty is designed so as to preserve the convexity of the total cost function to be minimized, thereby realizing the benefits of a convex optimization framework (reliable, robust algorithms, etc Compared to the conventional use of L1 norm penalty, the proposed non-convex penalty does not underestimate the true amplitude of signal values. We derive a fast proximal algorithm to implement the method. The proposed method is demonstrated on the suppression of artifacts in near infrared spectroscopic (NIRS) measures.
UR - http://www.scopus.com/inward/record.url?scp=85062110862&partnerID=8YFLogxK
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U2 - 10.1109/SPMB.2018.8615601
DO - 10.1109/SPMB.2018.8615601
M3 - Conference contribution
AN - SCOPUS:85062110862
T3 - 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings
BT - 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018
Y2 - 1 December 2018
ER -