Mound cells were those located on the initial clusters of cells generated by neurosphere dissociation, near mound represents cells that migrated away from the mound but still extended processes into the dense mound structures and sparse represents groups of cells that were in areas devoid of mounds.
Shearlet transform and wavelet transform are used successively to sparse represent the infrared and visible images, which can increase the signal sparsity obviously.
In order to sparse represent the signal matrix S using D, we aimed to learn a meaningful and over-completed dictionary matrix ( D{ in {mathbb{R}}}^{m times k} ) (k > m, k << n), with k being the dictionary atoms (i.e., components).
And then, these sparse signals are sparsely represented by wavelet basis.
Since the high-frequency NSCT coefficients have the sparse features, after being sparsely represented again by wavelet transform, their sparsity is enhanced.
The sparse vector β i is the expected value of a sparse vector representing the corresponding noiseless patch.
Therefore, the unknown sparse vector representing the acceleration space, u(n), can be obtained as the group sparse solution of the following linear formula: x ~ ( n ) = Ψ ( n ) u ( n ), n = 1, …, N. (22).
where E IGMRF d) and E sparse(d) represent the IGMRF and sparsity prior terms, respectively.
X = [ x 1, x 2, …, x M ] is the set of sparse codes representing the input samples Y in terms of columns of the learned dictionary D. The given sparsity level T0 restricts that each sample has fewer than T0 terms in its decomposition.
Features contributing to the first two estimated sparse PCs represent genes significantly over-represented in pathways typically deregulated between molecular subgroups of medulloblastoma.
