First, the similarity graph was constructed based on the representation coefficients rather than commonly used correlation.
Specifically, for the calculation of the representation coefficients optimal in terms of the SSIM index, we use their simple estimation scheme similar to some matching pursuit algorithms.
Besides, the most relevant variables identified by the representation coefficients support the robustness of the method.
In addition to the self-representation model, an extra sparsity constraint on the representation coefficients is emphasized to identify the most relevant variables.
The sparsity parameter λ in the sparse representation equation (Eq. 2) controls the number of nonzero entries in the representation coefficients.
First, sparse representation (SR) was employed to calculate the representation coefficients for each voxel by all the other voxels (Fig. 1a c).
Also, to show the usefulness of such sparse representation, we use the resulting prediction coefficients inside a multi-pulse excitation (MPE) coder and we show that the corresponding multi-pulse excitation source provides slightly better synthesis quality compared to the estimated excitation of the classical minimum variance synthesizer.
"If something else is going on, like flash heating, or something making [the material] have a lower coefficient of friction, this would affect any models that use the coefficient of friction," Singer said.
Finally, the EM 3D restraint was imposed on the GMM representation of each domain, using the cross-correlation coefficient between GMM representations of the EM volume and model components.
Moreover, to show a potential application of such sparse representation, we use the resulting linear prediction coefficients inside a multi-pulse synthesizer and show that the corresponding multi-pulse estimate of the excitation source results in slightly better synthesis quality when compared to the classical technique which uses the traditional non-sparse minimum variance synthesizer.
