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    Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness

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    SPM_reproducibility_Adali.pdf (4.322Mb)
    Links to Files
    https://ieeexplore.ieee.org/document/9810127
    Permanent Link
    https://doi.org/10.1109/MSP.2022.3163870
    http://hdl.handle.net/11603/25288
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    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    • UMBC Student Collection
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    Author/Creator
    Adali, Tülay
    Kantar, Furkan
    Akhonda, Mohammad Abu Baker Siddique
    Strother, Stephen
    Calhoun, Vince D.
    Acar, Evrim
    Author/Creator ORCID
    https://orcid.org/0000-0003-0594-2796
    https://orcid.org/0000-0003-0826-453X
    Date
    2022-06-28
    Type of Work
    29 pages
    Text
    journal articles
    postprints
    Citation of Original Publication
    T. Adali, F. Kantar, M. A. B. S. Akhonda, S. Strother, V. D. Calhoun and E. Acar, "Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness," in IEEE Signal Processing Magazine, vol. 39, no. 4, pp. 8-24, July 2022, doi: 10.1109/MSP.2022.3163870.
    Rights
    © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Abstract
    Data-driven solutions are playing an increasingly important role in numerous practical problems across multiple disciplines. The shift from the traditional model-driven approaches to those that are data driven naturally emphasizes the importance of the explainability of solutions, as, in this case, the connection to a physical model is often not obvious. Explainability is a broad umbrella and includes interpretability, but it also implies that the solutions need to be complete, in that one should be able to “audit” them, ask appropriate questions, and hence gain further insight about their inner workings [1]. Thus, interpretability, reproducibility, and, ultimately, our ability to generalize these solutions to unseen scenarios and situations are all strongly tied to the starting point of explainability.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.