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Robust Barron-Loss Tucker Tensor Decomposition

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posted on 24.12.2021, 12:38 authored by Mahsa Mozaffari, Panos P. MarkopoulosPanos P. Markopoulos

In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), which encompasses several well-known loss-functions with variable outlier resistance. The robustness of the proposed framework is corroborated by the presented numerical studies on synthetic and real data.

Funding

Collaborative Research: CDS&E: Theoretical Foundations and Algorithms for L1-Norm-Based Reliable Multi-Modal Data Analysis

Directorate for Computer & Information Science & Engineering

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(YIP) THEORY AND EFFICIENT ALGORITHMS FOR DYNAMIC AND ROBUST L1-NORM ANALYSIS OF TENSOR DATA

United States Air Force

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Email Address of Submitting Author

pxmeee@rit.edu

ORCID of Submitting Author

0000-0001-9686-779X

Submitting Author's Institution

Rochester Institute of Technology

Submitting Author's Country

United States of America

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