Open image in new window Fig. 8 Computational efficiency with different values of sparse parameter.
When the sparse nonzero locations are known, matrix approaches can be utilized to determine the values of sparse coefficients [58].
Various types of linear programming (LP) and some iterative algorithms, such as the IMAT with adaptive thresholding (IMAT), determine the number, positions, and values of sparse samples at the same time.
The sparse vector β i is the expected value of a sparse vector representing the corresponding noiseless patch.
In our experiments, the random projection together with sparse representation is repeated five times, and then the average value of five sparse solutions β is used for the final decision.
Then, the average value of five sparse solutions β is used to make a decision for y.
Then, we use the iteratively re-weighted least squares (IRLS) algorithm to estimate the value of the sparse coefficients.
In order to trade off stability and estimation performance of the LP- N LMS aLP- N LMS it is better to set the valgorithmhe sparse penalty as p = 0.5.
For a sparse channel, the value of the sparseness is close to 1 and for a dispersive channel, this value is close to 0. Therefore, the SC-RPAPA is begin{array}rcl@ mathrm{F}left left|hat{h}_{l}right|right)=frac{left|hat{h}_{l}right|}{left|hat{h}_{l}right|+hat{epsilon}(n)sigma_{text{max}}}, end{array} (21).
However, in some other applications, the values of the sparse signal rather than nonzero-valued locations cluster together.
We then estimate the values of the sparse vector with a modified iteratively re-weighted least-squares (IRLS) algorithm.
