Talk
Algorithms for $\ell_p$ Low-Rank Approximation
Flavio Chierichetti · Sreenivas Gollapudi · Ravi Kumar · Silvio Lattanzi · Rina Panigrahy · David Woodruff

Mon Aug 7th 06:27 -- 06:45 PM @ C4.4
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem. We obtain the first provably good approximation algorithms for this robust version of low-rank approximation that work for every value of $p$. Our algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, the running time, and the rank of the approximating matrix.

Author Information

Flavio Chierichetti (Sapienza University of Rome)
Sreenivas Gollapudi
Ravi Kumar (Google)
Silvio Lattanzi
Rina Panigrahy (Google)
David Woodruff

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