Sentence examples for amount of sparsity from inspiring English sources

where k>0 controls the amount of sparsity, and ℓ 0 pseudo-norm is defined by: leftVert xrightVert_{o}=left{ i:x_{i}neq0right}.

And they use restricted isometry property (RIP) constantly to determine the amount of sparsity needed for signal recovery as shown in reference [18].

In this section, we present an example to illustrate the amount of sparsity typically present in real-world multichannel spectra across subbands, channels and frames, and also jointly across multiple dimensions.

It can be observed that the multichannel reference spectra displays a fairly high amount of sparsity across all the three dimensions individually, with Gini indices on average above 0.5 (except for temporal sparsity in the surround-sound channels).

The L0-AbS method is based on a simple image model, which has two statistical parameters: α, controlling the amount of sparsity of the sparse approximation of the image in the representation domain, and σ r, which is the standard deviation of the sparse approximation from the actual vector of the image in the representation domain.

This may be due to the fact that the SPU scheme is the only one which completely ignores entire subbands when updating the filters, while the other schemes may allocate a few taps to each subband when the reference signal has a low amount of sparsity.

Secondly, it can be observed that typical real-world signals such as mono speech and five-channel movie and concert signals also display high amounts of sparsity.

The SPU scheme gives high values for highly sparse signals and very low values for signals with low amounts of sparsity, while the proposed DEA scheme performs similarly to the 3DM scheme for highly sparse signals and similarly to the FEA scheme for signals with low amounts of sparsity.

It can be observed that for signals with a high amount of spectral sparsity, such as the mono brown noise signal, the DEA scheme yields the best echo cancellation performance, while the 3DM and SPU schemes yield the poorest performance despite obtaining the highest values for the Closeness Measure.

We vary the number of shared causal SNPs across populations to see how the amount of shared sparsity pattern affects the performance of various methods.

We select the optimal parameters, λ and α, that respectively control the degree of sparsity and the amount of network constraint in the Net-Cox model using a 5-fold cross validation process.