Title
Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness
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 <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . 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.
Year
DOI
Venue
2022
10.1109/MSP.2022.3163870
IEEE Signal Processing Magazine
Keywords
DocType
Volume
broad umbrella,appropriate questions,reproducibility,explainability,model match,data-driven solutions,numerous practical problems,multiple disciplines,traditional model-driven approaches,physical model
Journal
39
Issue
ISSN
Citations 
4
1053-5888
0
PageRank 
References 
Authors
0.34
29
6
Name
Order
Citations
PageRank
Tülay Adali11690126.40
Furkan Kantar200.34
Mohammad Abu Baker Siddique Akhonda300.34
Stephen C. Strother439956.31
Vince D Calhoun52769268.91
Evrim Acar673634.24