According to the test results R-MCL is a considerable method against high sparsity and noise.
This new algorithm holds the advantages of simpler structure and higher sparsity.
So the channel coefficient corresponding to λ=2 has a higher sparsity.
Due to the higher sparsity, the class of sparse binary matrix is chosen as the measurement matrix in the paper.
Nevertheless, the random-node model still exhibits lower clustering and higher sparsity than the empirical book graphs.
Simulation results demonstrate that our optimized results can obey RIP with much higher sparsity than nonoptimized waveform and sensing matrix.
The algorithm differs from other Neumann spectral solvers by the high sparsity of the matrices, exploited in conjunction with the direct product structure of the problem.
We also show how to integrate the Navier Stokes equations using implicit schemes and Newton Krylov solvers, without impairing the high sparsity of the matrices.
In this paper, we propose a novel unsupervised feature selection algorithm, which retains the preserving power, and implements high sparsity and low redundancy in a unified manner.
The second inequality of Theorem 2 indicates that the higher sparsity of a vector x∗is, the higher RP of the modified-CS is.
