Sentence examples for sparsity of data from inspiring English sources

In the second half of this paper, we introduce a variant of the MLST designed to operate under sparsity of data.

However, the sparsity of data available to constrain the interpretation of this volcanic system hinders the application of standard 3D modelling techniques.

Furthermore, some of our comments were restricted by a sparsity of data or absence of knowledge on the applicability of specific aspects/points to the pediatric population.

The temporal and spatial sparsity of data that has been included in the development of the IRI model can be compensated for either by assimilation of new ionosonde results, or by upgrading the IRI model to include situations like space weather storms.

For instance, deriving drug repositioning from drug-disease interactions alone can be difficult due to the complexity, variability and sparsity of data currently available for the diseases, and to the intrinsic nature of publicly available gene expression data, which derive from patients already treated with other drugs in most of the cases.

One of the cyclones has only a single known point in its track due to a sparsity of data.

Previous studies [34 36] focused on more coarse spatio-temporal demographic sampling, usually covering extensive parts of cities and linking demographic information to social network check-ins and also cell-tower data, and given the higher sparsity of the data, the analysis are done on aggregate behaviours rather than modelling individual trajectories for prediction.

In 1996, Olshausen and Field [45] proposed an approach to learn the dictionary from a data in order to optimize the sparsity of the data.

For example, in [13], undersampled fMRI data are reconstructed using CS with sparsity of fMRI data in the wavelet domain, wherein orthogonal Daubechies wavelet is used as the sparsifying basis.

However, because of the sparsity of experimental data, the learning procedure yields many models, each explaining the data equally well.

A potential explanation for this lies in the sparsity of the data set: the rating matrix for IMDb contained just (0.91%) of all possible entries, whereas for the other data sets, this was the case for (4.16%) (MovieLens) and (1.26%) (BookCrossing).