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dc.contributor.authorAdali, Tülay
dc.contributor.authorKantar, Furkan
dc.contributor.authorAkhonda, Mohammad Abu Baker Siddique
dc.contributor.authorStrother, Stephen
dc.contributor.authorCalhoun, Vince D.
dc.contributor.authorAcar, Evrim
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US
dcterms.creatorhttps://orcid.org/0000-0003-0826-453Xen_US
dc.date.accessioned2022-08-05T20:49:26Z
dc.date.available2022-08-05T20:49:26Z
dc.date.issued2022-06-28
dc.description.abstractData-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.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9810127en_US
dc.format.extent29 pagesen_US
dc.genrejournal articlesen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m29bfh-h2bm
dc.identifier.citationT. 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.en_US
dc.identifier.urihttps://doi.org/10.1109/MSP.2022.3163870
dc.identifier.urihttp://hdl.handle.net/11603/25288
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.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.en_US
dc.titleReproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniquenessen_US
dc.typeTexten_US


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