Coupled Tensor-Matrix Recovery via Proximal Alternating Linearized Minimization, with an Application to Workforce Skill and Small-Business Health Estimation
arXiv:2607.10163v1 Announce Type: cross Abstract: We study recovery of a low-rank tensor $\mathcal{T}$ and a low-rank matrix $M$ from sparse, noisy observations. $\mathcal{T}$ and $M$ share one mode. We relax tensor rank using the nuclear norm of the mode-1 unfolding. This unfolding carries the coupling. It also has an exact proximal operator. We couple $\mathcal{T}$ and $M$ through a learned linear operator $G$. We prove a minimizer exists for the ridge-stabilized penalized objective. We prove
Record details
Published: 14 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Jobs & economy · Healthcare
Retrieved: 14 July 2026
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ethics.ai (14 July 2026), “Coupled Tensor-Matrix Recovery via Proximal Alternating Linearized Minimization, with an Application to Workforce Skill and Small-Business Health Estimation,” evidence record 1550, https://ethics.ai/record/1550 (originally published by arXiv cs.CY).
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