19 citations · 37 across the 8 of their papers we have counts for
8 papers
Sparse Recovery: The Square of Norms
Jianqing Jia, Ashley Prater-Bennette, Lixin Shen +1
This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the -model, which utilizes the squared norms for this pur…
Minimax Concave Penalty Regularized Adaptive System Identification
Bowen Li, Suya Wu, Erin E. Tripp +2
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communicat…
Semantic Communication with Conceptual Spaces
Dylan Wheeler, Erin E. Tripp, Balasubramaniam Natarajan
Despite the fact that Shannon and Weaver's Mathematical Theory of Communication was published over 70 years ago, all communication systems continue to operate at the first of three…
Asynchronous Multiuser Detection for SCMA with Unknown Delays: A Compressed Sensing Approach
Dylan Wheeler, Erin E. Tripp, Balasubramaniam Natarajan
Despite being the subject of a growing body of research, non-orthogonal multiple access has failed to garner sufficient support to be included in modern standards. One of the more…
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tian Tong, Cong Ma, Ashley Prater-Bennette +2
Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across vari…
The Proximity Operator of the Log-Sum Penalty
Ashley Prater-Bennette, Lixin Shen, Erin E. Tripp
The log-sum penalty is often adopted as a replacement for the pseudo-norm in compressive sensing and low-rank optimization. The hard-thresholding operator, i.e., the proxi…
Algorithmic Versatility of SPF-regularization Methods
Lixin Shen, Bruce W. Suter, Erin E. Tripp
Sparsity promoting functions (SPFs) are commonly used in optimization problems to find solutions which are assumed or desired to be sparse in some basis. For example, the l1-regula…
Structured Sparsity Promoting Functions
Lixin Shen, Bruce W. Suter, Erin E. Tripp
Motivated by the minimax concave penalty based variable selection in high-dimensional linear regression, we introduce a simple scheme to construct structured semiconvex sparsity pr…