Robust Barron-Loss Tucker Tensor Decomposition
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.
Collaborative Research: CDS&E: Theoretical Foundations and Algorithms for L1-Norm-Based Reliable Multi-Modal Data Analysis
Directorate for Computer & Information Science & EngineeringFind out more...
(YIP) THEORY AND EFFICIENT ALGORITHMS FOR DYNAMIC AND ROBUST L1-NORM ANALYSIS OF TENSOR DATA
United States Air ForceFind out more...