CUR Matrix Decomposition Based on Unequal Probability Adaptive Sampling and Stochastic SVD Decomposition
In high-dimensional big data matrix analysis,it is a common method to approximate the original data matrix with a few major components.These major components are linear combinations of matrix rows and columns,and it is difficult to explain the original characteristics of the data.Proposed in this paper to be ranging sampling combined with adaptive sampling method is suitable for the CUR matrix decomposition,and the random sampling method and matrix singular value decomposition(SVD)method,combining the matrix C and R obtained by sampling randomly SVD decomposition,in the control of computational complexity and improve the accuracy of low rank approximation reconstruction.The results show that the CUR matrix decomposition method based on the combination of unequal probability adaptive sampling and stochastic SVD decomposition has high accuracy and stability in low-rank approximation of matrices.
CUR matrix decomposition methodunequal probability adaptive samplingrandom SVD decompositionrelative errorcomputational complexity
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