{
  "id": 1550,
  "url": "https://arxiv.org/abs/2607.10163",
  "title": "Coupled Tensor-Matrix Recovery via Proximal Alternating Linearized Minimization, with an Application to Workforce Skill and Small-Business Health Estimation",
  "summary": "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 ",
  "authors": "Analee Miranda",
  "category": "research",
  "topics": "jobs-economy,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T04:00:00.000Z",
  "fetched_at": "2026-07-14T16:04:12.223Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/1550",
  "original_url": "https://arxiv.org/abs/2607.10163",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}