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English(EN) Structural Loss Metrics for Tensor Approximation via Matrix Low-Rank Approximation

新框架加速加权低秩矩阵逼近方法

作者 PulseAugur 编辑部 · [4 个来源] ·

本文将加权低秩矩阵逼近（WLRMA）作为经典低秩逼近和矩阵填充的推广。它提出了一个统一的框架，用于开发高效的优化方法，包括基于Nesterov动量和Anderson加速的加速算法，用于秩约束和核范数WLRMA问题。该研究还提出了大规模稀疏数据矩阵的可扩展实现和有效的秩判据，展示了在矩阵填充和广义线性低秩建模方面的显著计算收益和应用。 AI

影响 引入了用于统计建模和矩阵填充的新计算方法，可能提高机器学习任务的效率。

排序理由 该条目是一篇学术论文，详细介绍了加权低秩矩阵逼近的新方法和应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新框架加速加权低秩矩阵逼近方法

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该条目是一篇学术论文，详细介绍了加权低秩矩阵逼近的新方法和应用。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Lavinia Ghita, Dhruv Desai, Jake Goldberg, Roman Yokunda Enzmann ·

    低秩依赖分解通过加速对称非负矩阵分解

    arXiv:2607.24518v1 Announce Type: new Abstract: Symmetric non-negative matrix factorization (SymNMF) recovers latent group structure from a dependence matrix, but its dense, quadratic-memory objective has confined prior work to moderate sizes. We present a large-scale GPU study o…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    基于矩阵低秩近似的张量近似的结构化损失度量

    Matricized low-rank approximation via SVD is a standard surrogate for tensor decompositions, but entry-wise reconstruction error fails to capture multiway geometric degradation. Under an orthogonal Tucker model, we characterize this degradation using two metrics: cross-mode Direc…

  3. arXiv stat.ML TIER_1 English(EN) · Elena Tuzhilina, Trevor Hastie ·

    加权低秩矩阵近似：加速与应用

    arXiv:2109.11057v2 Announce Type: replace Abstract: Weighted low-rank matrix approximation (WLRMA) generalizes classical low-rank approximation and matrix completion by allowing arbitrary elementwise weights. Such formulations arise naturally in a broad class of statistical model…

  4. arXiv cs.CV TIER_1 English(EN) · Hiroki Hasegawa ·

    通过矩阵低秩近似进行张量近似的结构化损失度量

    arXiv:2607.24009v1 Announce Type: new Abstract: Matricized low-rank approximation via SVD is a standard surrogate for tensor decompositions, but entry-wise reconstruction error fails to capture multiway geometric degradation. Under an orthogonal Tucker model, we characterize this…

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