Sentence examples for sparse procedures from inspiring English sources

The entire sparse procedures have been coded in standard Fortran language.

It consists in two steps: first, a selection step performed with a sparse procedure; and second, a resampling step whose purpose is to stabilize the selection for more robustness in the reconstructed network.

This means that all sparse approximation procedures could be done in ({mathbb {R}}^{m_{n}}) instead of ({mathbb {R}}^{d}), and so setting (m=frac {1}{N} sum {m_{n}}), the cost estimate for ITKrMM reduces to O(m K N+S 2 m N) and for wKSVD to O(S m K N).

In general, sparse data procedures, typical in psychopathology research, suggest wide posterior distributions, reflecting high uncertainty in the parameter values.

Provided fast methods are used to generate the original alignment, the meta-alignment procedure of M-Coffee can use a sparse DP procedure that takes advantage of the strong agreement between the considered alignments.

The second component is the time consumption of the sparse reconstruction procedure.

The dictionary only increases the run time of the sparse reconstruction procedure with the increase of basis.

In this section, we first review the basic theory of CS and PCA and we subsequently illustrate the sparse transformation procedure for SAR raw data.

This paper investigates a computational model that combines a frontend based on an auditory model with an exemplar-based sparse coding procedure for estimating the posterior probabilities of sub-word units when processing noisified speech.

In Section "Joint PCA and CS for SAR raw data compression", we summarized a mathematic description of CS and PCA theory and the sparse transform procedure for SAR raw data was proposed.

In Sys1, Sys2, and Sys3, we capitalize on the assumption that the sparse classification procedure can harness the differences between speech and noise in the modulation spectra without being given any specific information.