Sentence examples for sparsity across from inspiring English sources

From these spectrograms, we first analyze the sparsity across subbands (spectral sparsity), across frames (temporal sparsity) and across channels (spatial sparsity).

The potential for exploiting signal sparsity across three dimensions, namely time, frequency, and channels, is analyzed.

We propose a differential versioning based data storage (DiVers) architecture for distributed storage systems, which relies on a novel erasure coding technique that exploits sparsity across versions.

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).

Fu et al. [19] proposed an effective model based on DL for hyperspectral image (HSI) denoising by considering sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space.

This provides the motivation to exploit sparsity jointly across subbands, channels and frames for the purpose of tap selection.

A multitask regression with ℓ1/ℓ2-regularization was proposed to learn a joint sparsity pattern across multiple tasks (Obozinski et al., 2006).

This shows that we can significantly benefit from the use of the L1/ L2 penalty that combines information on sparsity pattern across multiple populations.

From this figure, it can be clearly observed that the multichannel reference spectra exhibit even higher levels of sparsity when analyzed across multiple dimensions, with Gini indices on average above 0.85.

However, no such sparsity is expected in general across the columns.

This is an important consideration, as we want to compare the sparsity of the reference spectra across the different dimensions of subbands, channels and frames.