The Challenge of Small Data: Dynamic Mode Decomposition, Redux
Amirhossein Karimi, Tryphon T. Georgiou
TL;DR
This paper critically examines the assumptions of Dynamic Mode Decomposition (DMD), highlighting its limitations and proposing measures to assess its applicability in small data scenarios.
Contribution
It provides a detailed analysis of DMD's assumptions and offers guidelines to determine when DMD is appropriate for small data applications.
Findings
Identifies key caveats in DMD methodology.
Proposes measures to evaluate DMD applicability.
Highlights limitations in small data contexts.
Abstract
We revisit the setting and the assumptions that underlie the methodology of Dynamic Mode Decomposition (DMD) in order to highlight caveats as well as potential measures of when the applicability is warranted.
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Taxonomy
TopicsMachine Fault Diagnosis Techniques · Influenza Virus Research Studies