Minimization of the q-ratio sparsity with 1 < q ≤ ∞ for signal recovery

2020/10/07 by Zhiyong Zhou, Jun Yu, Zhou, Zhiyong +1 · 2 citations
Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Image and Signal Denoising Methods #Blind Source Separation Techniques

paper · pdf · doi:10.48550/arxiv.2010.03402

Abstract

In this paper, we propose a general scale invariant approach for sparse signal recovery via the minimization of the q-ratio sparsity. When 1 < q ≤ ∞, both the theoretical analysis based on q-ratio constrained minimal singular values (CMSV) and the practical algorithms via nonlinear fractional programming are presented. Numerical experiments are conducted to demonstrate the advantageous performance of the proposed approaches over the state-of-the-art sparse recovery methods.

Citations

• Nearly unbiased variable selection under minimax concave penalty
• Sparse Approximate Solutions to Linear Systems
• Sparse filtering with the generalized lp / lq norm and its applications to the condition monitoring of rotating machinery

Cited by

• Difference-of-Convex Elastic Net for Compressed Sensing
• An Efficient ADMM Method for Ratio-Type Nonconvex and Nonsmooth Minimization in Sparse Recovery

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