430 citations · 1.6k across the 21 of their papers we have counts for
8 papers · 1 filter
The Douglas-Rachford Algorithm for Weakly Convex Penalties
İlker Bayram, Ivan W. Selesnick
The Douglas-Rachford algorithm is widely used in sparse signal processing for minimizing a sum of two convex functions. In this paper, we consider the case where one of the functio…
Enhanced Sparsity by Non-Separable Regularization
Ivan W. Selesnick, Iker Bayram
This paper develops a convex approach for sparse one-dimensional deconvolution that improves upon L1-norm regularization, the standard convex approach. We propose a sparsity-induci…
Enhanced Low-Rank Matrix Approximation
Ankit Parekh, Ivan W. Selesnick
This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with non-convex regularization. We employ parameterized non-convex penalty functions…
Detection of Faults in Rotating Machinery Using Periodic Time-Frequency Sparsity
Yin Ding, Wangpeng He, Binqiang Chen +2
This paper addresses the problem of extracting periodic oscillatory features in vibration sig- nals for detecting faults in rotating machinery. To extract the feature, we propose a…
Sparsity-based Algorithm for Detecting Faults in Rotating Machines
Wangpeng He, Yin Ding, Yanyang Zi +1
This paper addresses the detection of periodic transients in vibration signals for detecting faults in rotating machines. For this purpose, we present a method to estimate periodic…
Sparsity-based Correction of Exponential Artifacts
Yin Ding, Ivan W. Selesnick
This paper describes an exponential transient excision algorithm (ETEA). In biomedical time series analysis, e.g., in vivo neural recording and electrocorticography (ECoG), some me…
Convex Fused Lasso Denoising with Non-Convex Regularization and its use for Pulse Detection
Ankit Parekh, Ivan W. Selesnick
We propose a convex formulation of the fused lasso signal approximation problem consisting of non-convex penalty functions. The fused lasso signal model aims to estimate a sparse p…
Convex Denoising using Non-Convex Tight Frame Regularization
Ankit Parekh, Ivan W. Selesnick
This paper considers the problem of signal denoising using a sparse tight-frame analysis prior. The L1 norm has been extensively used as a regularizer to promote sparsity; however,…