Instead of assembling a sparse matrix and using it for matrix vector products, the operation is applied by cell-wise quadrature.
Hence, to simultaneously reduce the complexity and select a complex vector g s instead of a Boolean one, we use sparse signal recovery techniques.
To address the aforementioned problems, this paper develops a simple but effective tracking-by-detection scheme by exploring the distribution of sparse coefficients instead of the sparse representation error for similarity measure.
Instead of utilizing sparse warping matrix [29, 30, 33], GAC models reformulate homography as convolution related to slice and fiber and can achieve better trade-off between computational cost and memory complexity.
Sparseness is obtained by a component-wise independent Laplace distribution (Hyvärinen and Oja, 1999), which is now used as a prior on the factors instead of the Gaussian: Sparse loadings λ i and, therefore sparse Λ, are achieved by two alternative strategies.
Therefore, instead of imposing this sparse constraint, we consider to minimize an optimization model having the ℓ 0-norm as its objective function.
Our main improvements over previous work consist of employing a Laplace prior instead of a simpler Gaussian one, encoding the key property of sparse connectivity of regulatory networks within the model, and of actively designing rather than randomly choosing experiments.
Instead of waiting for sparse roles to come by, I create opportunities for myself such as The John Maucere Show, No Ordinary Hero: the SuperDeafy Movie, DEAFYWOOD to name a few.
