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New Wedge Sampling method boosts tensor completion efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Wedge Sampling, a novel non-adaptive sampling scheme designed for efficient low-rank tensor completion. This new method utilizes structured length-two patterns, known as wedges, within a bipartite sampling graph to strengthen spectral signals. The approach promises polynomial-time algorithms capable of achieving recovery with nearly linear sample complexity, significantly improving upon traditional uniform sampling methods. AI

RANK_REASON The cluster contains a research paper detailing a new statistical method for tensor completion. [lever_c_demoted from research: ic=1 ai=0.7]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Hengrui Luo, Anna Ma, Ludovic Stephan, Yizhe Zhu ·

    Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity

    arXiv:2602.05869v2 Announce Type: replace Abstract: We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion. We study recovery of an order-$k$ low-rank tensor of dimension $n \times \cdots \times n$ from a subset of its entries. Unlike the s…

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