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Convex Recovery of Tensors Using Nuclear Norm Penalization

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Latent Variable Analysis and Signal Separation (LVA/ICA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9237))

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Abstract

The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex analysis and optimization over spaces of tensors is now gaining much interest due to its potential applications to signal processing, statistics and engineering. The goal of this paper is to present an applications to the problem of low rank tensor recovery based on linear random measurement by extending the results of Tropp [6] to the tensors setting.

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References

  1. Chrétien, S., Wei, T.: Von neumann’s inequality for tensors, arXiv preprint arXiv:1502.01616 (2015)

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  6. Tropp, J.A.: Convex recovery of a structured signal from independent random linear measurements, arXiv preprint arXiv:1405.1102 (2014)

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Authors and Affiliations

  1. Laboratoire de Mathématiques de Besancon, Université de Franche-Comté, 16 Route de Gray, 25000, Besancon, France

    Stéphane Chrétien & Tianwen Wei

Authors
  1. Stéphane Chrétien
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  2. Tianwen Wei
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Corresponding author

Correspondence to Tianwen Wei .

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Editors and Affiliations

  1. Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  2. Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  3. Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  4. The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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Chrétien, S., Wei, T. (2015). Convex Recovery of Tensors Using Nuclear Norm Penalization. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-22482-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22481-7

  • Online ISBN: 978-3-319-22482-4

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