arXiv:1302.2767
Coherence and sufficient sampling densities for reconstruction in compressed sensing
원문 초록
We give a new, very general, formulation of the compressed sensing problem in terms of coordinate projections of an analytic variety, and derive sufficient sampling rates for signal reconstruction. Our bounds are linear in the coherence of the signal space, a geometric parameter independent of the specific signal and measurement, and logarithmic in the ambient dimension where the signal is presented. We exemplify our approach by deriving sufficient sampling densities for low-rank matrix completion and distance matrix completion which are independent of the true matrix.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
Linear Recursive Feature Machines provably recover low-rank matrices
Teaching Arithmetic to Small Transformers
토론
이 논문과 연결된 포럼 글이 아직 없습니다.