Andrew Tulloch

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A few iterations of alternating least squares with a random starting point provably suffice to produce nearly optimal spectral-and Frobenius-norm accuracies of low-rank approximations to a matrix; iterating to convergence is unnecessary. Thus, software implementing alternating least squares can be retrofitted via appropriate setting of parameters to(More)
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