Exact(1)
To accelerate the computation process of rough set approximations, this paper first presents the boolean matrix representation of the lower and upper approximations in the composite information system, then designs a parallel method for computing approximations based on matrix, and implements it on Multi-GPU.
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We observe that these algorithms are based on matrix computations and, hence, are inefficient to implement with the restrictive programming and communication interface of such frameworks.
Matrix permeability was based on matrix porosity and permeability porosity correlations.
We have presented a novel approach to solve the EEG inverse problem, which is based on matrix factorization and regularization.
In order to do so, we propose a new method based on matrix factorization and regularization, with the aim of recovering the latent structure of the BES matrix.
Meanwhile, two detection algorithms respectively based on matrix completion and jointly sparse recovery were also proposed to recover the channel occupancy, while the relatively faster jointly sparse recovery algorithm still involves multiple constrained optimization sub-problems.
Our approach in this article is based on matrix completion and is best motivated by the limitations of the existing methods discussed next.
Like the method by Chen et al. (2012), the new method is based on matrix operations and is invariant to linear transformations.
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