Sentence examples for sparsity measure from inspiring English sources

Rural schools, in contrast, which suffer particular hardships because they are often small and isolated and therefore attract less funding and support, stand to gain from the new arrangements and a revised "sparsity measure" that will provide additional funding.

The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density.

A data sparsity measure is used to divide the data before distributing the columns in the network over many computing nodes based on the MapReduce framework.

We demonstrate the sparsity measure of utilizing l 1 norm can obtain better reconstruction than l 0 norm for our framework heuristically.

To measure the sparsity of a signal, the l1 norm is the most used sparsity measure thanks to its convexity [13].

The sparsity measure in (15) has been used for applications such as NMF matrix decomposition with more part-based properties [56]; however, it has never been used for feature extraction application.

The mathematical building blocks are coorbit theory, multi- -modulation frames, and the concept of joint sparsity measures.

In this experiment, we compare the video reconstruction result of the proposed object detection model with three known video reconstruction sparsity measures: 2DTV, DWT, and 2DTV + DWT.

The decision tree in Fig. 1 also recommends employing J48 for problems whose sparsity (measured by T2) is below a given threshold (the higher the value of T2, the denser the dataset).

The paper entitled "Coorbit theory, multi- alpha-modulation frames and the concept of joint sparsity for medical multichannel data analysis" by Stephan Dahlke et al. presents a signal processing technique that detects and separates signal components such as mMCG, fMCG, or MMG by integrating coorbit theory, multi-α-modulation frames, and the concept of joint sparsity measures.

Approaches like ICA are quite often linear, and can thus be quite readily interpreted, and the most widely-used ICA approaches optimize for both independent and sparsity measures with considerable success [ 63].