Sentence examples for sparsity feature from inspiring English sources

Moreover, by utilizing the sparsity feature of the PCE approach, the implementation of a least angle regression (LAR) concept leads to a significant reduction in computational costs.

As a result, we can take advantage of the block sparsity feature, building a sparse reconstruction SAR imaging algorithm based on Kronecker dictionary.

According to the spatial sparsity feature of array signals [29 31], an array signal at time instant m is spatially sparse.

Furthermore, the proposed technique can be enhanced by thresholding the CIR to a certain value and also by exploiting the common sparsity feature inherent in the system channel.

The sparsity feature is zero if and only if a vector contains a single non-zero component (i.e., maximum sparsity), and is negative infinity if and only if all the components are equal (i.e., minimum sparsity).

According to the above research achievements in SAR raw data compressing based on CS theory, we can see that the SAR imagery data usually have poor sparsity feature and looking for suitable sparse transformation basis for SAR images is extremely significant.

An illustrative example for this are jellium slabs, where we discuss various sparsity features of matrices related to Coulomb and exchange potentials.

With the successful usage of sparse models in image and video classification and understanding, imposing structural sparsity in feature selection has been widely investigated during the past years.

In addition, to separate different approximate rigid parts generated from different human activities, a novel sparsity induced feature selection scheme is introduced.

Additionally, the adaptive structural clustering sparse dictionary learning technique, which utilizes k-Nearest-Neighbor (kNN) clustering and principal component analysis (PCA) learning, is adopted to further enable sufficient sparsity of feature information.

The best performance of the k-mer based method is with k equal to 5. Larger values of k cause a significant degradation of the performance of the k-mer based method, presumably due to overfitting associated with the exponentially growing number of features and sparsity of the feature vectors when k increases.