ASILOMAR21_Robust_Tucker_Decomposition__preprint_.pdf (698.9 kB)
Robust Barron-Loss Tucker Tensor Decomposition
preprint
posted on 2021-12-24, 12:38 authored by Mahsa Mozaffari, Panos P. MarkopoulosPanos P. MarkopoulosIn 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
Find out more...(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.eduORCID of Submitting Author
0000-0001-9686-779XSubmitting Author's Institution
Rochester Institute of TechnologySubmitting Author's Country
- United States of America