Data_Sheet_1_On the Construction of Sparse Matrices From Expander Graphs.pdf
We revisit the asymptotic analysis of probabilistic construction of adjacency matrices of expander graphs proposed in Bah and Tanner [1]. With better bounds we derived a new reduced sample complexity for d, the number of non-zeros per column of these matrices (or equivalently the left-degree of the underlying expander graph). Precisely d=O(logs(N/s)); as opposed to the standard d=O(log(N/s)), where N is the number of columns of the matrix (also the cardinality of set of left vertices of the expander graph) or the ambient dimension of the signals that can be sensed by such matrices. This gives insights into why using such sensing matrices with small d performed well in numerical compressed sensing experiments. Furthermore, we derive quantitative sampling theorems for our constructions which show our construction outperforming the existing state-of-the-art. We also used our results to compare performance of sparse recovery algorithms where these matrices are used for linear sketching.
History
Usage metrics
Categories
- Statistics
- Mathematical Physics not elsewhere classified
- Ordinary Differential Equations, Difference Equations and Dynamical Systems
- Computation Theory and Mathematics
- Financial Mathematics
- Applied Mathematics not elsewhere classified
- Optimisation
- Numerical and Computational Mathematics not elsewhere classified
- Applied Statistics
- Numerical Computation
- Computation Theory and Mathematics not elsewhere classified