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Faster SVD-Truncated Least-Squares Regression

Christos Boutsidis,M. Magdon-Ismail

Published 2014 in arXiv.org

ABSTRACT

We develop a fast algorithm for computing the "SVD-truncated" regularized solution to the least-squares problem: $ \min_{\x} \TNorm{\matA \x - \b}. $ Let $\matA_k$ of rank $k$ be the best rank $k$ matrix computed via the SVD of $\matA$. Then, the SVD-truncated regularized solution is: $ \x_k = \pinv{\matA}_k \b. $ If $\matA$ is $m \times n$, then, it takes $O(m n \min\{m,n\})$ time to compute $\x_k $ using the SVD of \math{\matA}. We give an approximation algorithm for \math{\x_k} which constructs a rank-\math{k} approximation $\tilde{\matA}_{k}$ and computes $ \tilde{\x}_{k} = \pinv{\tilde\matA}_{k} \b$ in roughly $O(\nnz(\matA) k \log n)$ time. Our algorithm uses a randomized variant of the subspace iteration. We show that, with high probability: $ \TNorm{\matA \tilde{\x}_{k} - \b} \approx \TNorm{\matA \x_k - \b}$ and $\TNorm{\x_k - \tilde\x_k} \approx 0. $

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    arXiv.org

  • Publication date

    2014-01-02

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1401.0417
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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