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Estimation of matrices with row sparsity

Author

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  • Olga Klopp

    (CREST, ENSAE)

  • Alexandre Tsybakov

    (CREST, ENSAE)

Abstract

An increasing number of applications is concerned with recovering a sparse matrix from noisy observations. In this paper, we consider the setting where each row of the unknown matrix is sparse. We establish minimax optimal rates of convergence for estimating matrices with row sparsity. A major focus in the present paper is on the derivation of lower bounds.

Suggested Citation

  • Olga Klopp & Alexandre Tsybakov, 2016. "Estimation of matrices with row sparsity," Working Papers 2016-11, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2016-11
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    File URL: http://crest.science/RePEc/wpstorage/2016-11.pdf
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    References listed on IDEAS

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    1. Olga Klopp, 2012. "Noisy Low-rank Matrix Completion with General Sampling Distribution," Working Papers 2012-06, Center for Research in Economics and Statistics.
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