60 citations · 234 across the 39 of their papers we have counts for
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cs.DC2016★ 1 cited
Randomized algorithms for distributed computation of principal component analysis and singular value decomposition
Huamin Li, Yuval Kluger, Mark Tygert
Randomized algorithms provide solutions to two ubiquitous problems: (1) the distributed calculation of a principal component analysis or singular value decomposition of a highly re…
math.NA2016
Accurate principal component analysis via a few iterations of alternating least squares
Arthur Szlam, Andrew Tulloch, Mark Tygert
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 approximat…
cs.LG2016★ 1 cited
Poor starting points in machine learning
Mark Tygert
Poor (even random) starting points for learning/training/optimization are common in machine learning. In many settings, the method of Robbins and Monro (online stochastic gradient…