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In every T s, the MS utilizes the compression matrix to compress high-dimensional CSI into low-dimensional representation.
In the proposed scheme, the mobile station (MS) generates a compression matrix by operating PCA on the channel state information (CSI) over a long-term period, and utilizes the compression matrix to compress the spatially correlated high-dimensional CSI into a low-dimensional representation.
In our algorithm, we use a bit matrix to compress the dataset and mapping the mining algorithm on the systolic arrays architecture.
In the following experiments, we used Bernoulli random measurement matrix to compress the sparse signal and analyze the reconstruction performance of all the reconstruction algorithms.
In the following experiments, we used Gaussian random measurement matrix to compress the sparse signal at first and analyze the reconstruction performance of all the reconstruction algorithms.
The Watson's method uses a perceptual quantization matrix to compress color images, in which the quantization step size of each subband of the color image is determined by the base JND [14] within the subband.
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Then, we employ Gaussian random-matrix to compress EMV and obtain the measurements.
Moreover, a Boolean matrix called as the classification matrix is used to compress the classification resulted by pruned ensemble on the dataset.
Random Gaussian measurement matrix is used to compress the x i from N to M and its relation with the compressed data can be given by {y}_i=varPhi {x}_i (7 where y i ∈ R M × 1 and Φ ∈ R M × N is a random Gaussian matrix.
The associated matrix is easy to compress and thus it is easy to know the size of a sub-graph, so we use the associated matrix to store real graphs and random graphs.
After that, two different strategies are used to compress this matrix.
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