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Lower Bounds for Column Matrix Approximations

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Abstract

We show a connection between lower and upper bounds on the column matrix approximation accuracy and the bounds on the norms of the pseudoinverses of the submatrices of orthogonal matrices. This connection is exploited to derive lower bounds for column approximations accuracy in spectral and Frobenius norms.

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REFERENCES

  1. C. Boutsidis, M. W. Mahoney, and P. Drineas, “Unsupervised feature selection for principal components analysis,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008), pp. 61–69.

  2. A. K. Farahat, A. Ghodsi, and M. S. Kamel, “Efficient greedy feature selection for unsupervised learning,” Knowl. Inf. Syst. 35, 285–310 (2012).

    Article  Google Scholar 

  3. S. Kumar, M. Mohri, and A. Talwalkar, “Sampling methods for the Nyström method,” J. Mach. Learn. Res. 13 (1), 981–1006 (2012).

    MathSciNet  Google Scholar 

  4. T. F. Chan and P. C. Hansen, “Some applications of the rank revealing QR factorization,” SIAM J. Sci. Stat. Comput. 13 (3), 727–741 (1992).

    Article  MathSciNet  Google Scholar 

  5. C. Boutsidis and D. P. Woodruff, “Optimal CUR matrix decompositions,” in Proceedings of the 46th Annual ACM Symposium on Theory of Computing (ACM, 2014), pp. 353–362.

  6. S. A. Goreinov, E. E. Tyrtyshnikov, and N. L. Zamarashkin, “A theory of pseudo-skeleton approximations,” Linear Algebra Appl. 261, 1–21 (1997).

    Article  MathSciNet  Google Scholar 

  7. A. Deshpande, L. Rademacher, S. Vempala, and G. Wang, “Matrix approximation and projective clustering via volume sampling,” Theory Comput. 2, 225–247 (2006).

    Article  MathSciNet  Google Scholar 

  8. V. Guruswami and A. K. Sinop, “Optimal column-based low-rank matrix reconstruction,” in Proceedings of the 2012 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (ACM, 2012), pp. 1207–1214.

  9. C. Boutsidis, P. Drineas, and M. Magdon-Ismail, “Near-optimal column-based matrix reconstruction,” SIAM J. Comput. 43 (2), 687–717 (2014).

    Article  MathSciNet  Google Scholar 

  10. H. Avron and C. Boutsidis, “Faster subset selection for matrices and applications,” SIAM J. Matrix Anal. A 34 (4), 1464–1499 (2013).

    Article  MathSciNet  Google Scholar 

  11. A. I. Osinsky and N. L. Zamarashkin, “Pseudo-skeleton approximations with better accuracy estimates,” Linear Algebra Appl. 537, 221–249 (2018).

    Article  MathSciNet  Google Scholar 

  12. A. Y. Michalev and I. V. Oseledets, “Rectangular maximum-volume submatrices and their applications,” Linear Algebra Appl. 538, 187–211 (2018).

    Article  MathSciNet  Google Scholar 

  13. K. Hamm and L. Huang, “Perturbations of CUR decompositions,” SIAM J. Matrix Anal. A 42 (1), 351–375 (2021).

    Article  MathSciNet  Google Scholar 

  14. A. Deshpande and S. Vempala, “Adaptive sampling and fast low-rank matrix approximation,” Approximation, Randomization Combinatorial Optim. 4110 (3), 292–303 (2006).

    Article  MathSciNet  Google Scholar 

  15. M. Gu and S. C. Eisenstat, “Efficient algorithms for computing a strong rank-revealing QR factorization,” SIAM J. Sci. Comput. 17 (4), 848–869 (1996).

    Article  MathSciNet  Google Scholar 

  16. S. A. Goreinov, “On cross approximation of multi-index arrays,” Dokl. Math. 77 (3), 404–406 (2008).

    Article  MathSciNet  Google Scholar 

  17. N. L. Zamarashkin and A. I. Osinsky, “On the accuracy of cross and column low-rank maxvol approximations in average,” Comput. Math. Math. Phys. 61 (5), 786–798 (2021).

    Article  MathSciNet  Google Scholar 

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Funding

The work was supported by the Russian Science Foundation, project no. 19-11-00338, https://rscf.ru/en/project/19-11-00338/.

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Correspondence to A. Osinsky.

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Osinsky, A. Lower Bounds for Column Matrix Approximations. Comput. Math. and Math. Phys. 63, 2024–2037 (2023). https://doi.org/10.1134/S0965542523110167

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  • DOI: https://doi.org/10.1134/S0965542523110167

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