DOE OSTI · 3026710
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
Abstract
In this paper, we propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce an order orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm guaranteed to converge to a stationary point. The unique and common representations learned by perTucker reveal intrinsic statistical patterns in data and provide valuable information for a wide range of downstream analytics, including anomaly detection, source classification, and clustering. We demonstrate perTucker’s effectiveness through a simulation study and two case studies on solar flare detection and tonnage signal classification.
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Hu, Jiuyun [Arizona State Univ., Tempe, AZ (United States)], Shi, Naichen [Univ. of Michigan, Ann Arbor, MI (United States)], Al Kontar, Raed [Univ. of Michigan, Ann Arbor, MI (United States)] (ORCID:000000024546324X), Yan, Hao [Arizona State Univ., Tempe, AZ (United States)] (ORCID:0000000243227323). 2025-03-19. Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data. https://doi.org/10.1080/00401706.2025.2453206
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