We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entry-wise ℓp-approximation error, for any P ≥ 1; the case p = 2 is the classical SVD problem. We obtain the first provably good approximation algorithms for this version of low-rank approximation that work for every value of p ≥ 1, including p = σ. 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.
Algorithms for ℓp Low Rank Approximation / Chierichetti, Flavio; Gollapudi, Sreenivas; Kumar, Ravi; Lattanzi, Silvio; Panigrahy, Rina; Woodruff, David P.. - 70:(2017), pp. 806-814. (Intervento presentato al convegno 34th International Conference on Machine Learning tenutosi a Sydney; Australia).
Algorithms for ℓp Low Rank Approximation
CHIERICHETTI, FLAVIO
;LATTANZI, SILVIO;
2017
Abstract
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entry-wise ℓp-approximation error, for any P ≥ 1; the case p = 2 is the classical SVD problem. We obtain the first provably good approximation algorithms for this version of low-rank approximation that work for every value of p ≥ 1, including p = σ. 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.| File | Dimensione | Formato | |
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Chierichetti_Algorithms_2017.pdf
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Note: http://proceedings.mlr.press/v70/chierichetti17a/chierichetti17a.pdf
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