@inproceedings{9977a232a5704dd1ad2f37a702a9fe60,
title = "Fused lasso with a non-convex sparsity inducing penalty",
abstract = "The fused lasso problem involves the minimization of the sum of a quadratic, a TV term and an ℓ1 term. The solution can be obtained by applying a TV denoising filter followed by soft-thresholding. However, soft-thresholding introduces a certain bias to the non-zero coefficients. In order to prevent this bias, we propose to replace the ℓ1 penalty with a non-convex penalty. We show that the solution can similarly be obtained by applying a modified thresholding function to the result of the TV-denoising filter.",
keywords = "Fused lasso, audio denoising, non-convex penalty, thresholding, total variation denoising",
author = "Ilker Bayram and Chen, {Po Yu} and Selesnick, {Ivan W.}",
year = "2014",
doi = "10.1109/ICASSP.2014.6854384",
language = "English (US)",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4156--4160",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}