107 citations · 158 across the 2 of their papers we have counts for
4 papers
Improved Sparse Low-Rank Matrix Estimation
Ankit Parekh, Ivan W. Selesnick
We address the problem of estimating a sparse low-rank matrix from its noisy observation. We propose an objective function consisting of a data-fidelity term and two parameterized…
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…
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,…