Lecture 15 : Additive - error Low - rank Matrix Approximation with Sampling and Projections
نویسنده
چکیده
• A spectral norm bound for reconstruction error for the basic low-rank approximation random sampling algorithm. • A discussion of how similar bounds can be obtained with a variety of random projection algorithms. • A discussion of possible ways to improve the basic additive error bounds. • An iterative algorithm that leads to additive error with much smaller additive scale. This will involve using the additive error sampling algorithm in an iterative manner in order to drive down the additive error quickly as a function of the number of iterations.
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