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Recently, researchers implemented sparse component analysis (SCA) to improve the method of blind image separation [19, 20].
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The R package, sparcl [ 20], was then used to implement sparse k-means clustering using the squared Euclidean distance as the dissimilarity measure between objects, 20 random starts and a maximum of 20 iterations of the k-means algorithm.
Moreover, the proposed controllers are implemented through sparse communication networks and thus meet the plug-and-play feature of microgrid power systems.
Finite difference approximations to these derivatives can then be implemented using sparse matrices, thus removing the need to store and manipulate large full matrices.
Motion estimation, as the other essential step in video stabilization, can be implemented by sparse feature tracking [1] or block matching [2, 3].
To reduce this in SOAPdenovo2, we implemented a sparse de Bruijn graph method [ 8] (see Additional file 1: Supplementary Method 2), where reads are cut into k-mers and a large number of the linear unique k-mers are combined as a group instead of being stored independently.
We re-implemented the sparse CCA algorithm proposed in [12] using Matlab.
Mairal et al. [26] implement simultaneous sparse coding which combines between non-local means approach and dictionary learning.
Facing the difficulty to optimize such nonorthogonal and nonlinear transforms, we implement a sparse approximation scheme inspired from the functional architecture of the primary visual cortex.
The R functions used to implement integrative sparse k-means clustering are available upon request.
Using NVIDIA's CUDA platform we have implemented a Wilson Dirac sparse matrix vector product that performs at up to 40, 135 and 212 Gflops for double, single and half precision respectively on NVIDIA's GeForce GTX 280 GPU.
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