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English(EN) Joint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain Imaging

为逻辑矩阵回归引入新的凸优化框架

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的逻辑标量对矩阵回归的凸优化框架。该方法结合了核范数和 $\ell_1$ 范数惩罚，以同时强制估计系数矩阵中的低秩和稀疏结构。推导了一种基于交替方向乘子法 (ADMM) 的算法来有效地解决该问题并建立理论性质。该框架应用于脑成像数据，以识别具有酒精使用障碍家族史的受试者的特征性功能性大脑连接结构。 AI

影响 引入了一种分析复杂数据的新统计方法，有望改善神经影像学及相关领域的模式识别。

排序理由 该集群包含一篇详细介绍新统计方法及其应用的学术论文。[lever_c_demoted from research: ic=2 ai=0.4]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

为逻辑矩阵回归引入新的凸优化框架

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该集群包含一篇详细介绍新统计方法及其应用的学术论文。[lever_c_demoted from research: ic=2 ai=0.4]
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Damian Brzyski, Aaron Cohen, Zijian Wang, Mario Dzemidzic, David A. Kareken, Jaroslaw Harezlak ·

    用于脑成像的带联合核和$\ell_1$正则化的逻辑矩阵回归

    arXiv:2606.14436v1 Announce Type: cross Abstract: We introduce a new convex optimization framework for logistic scalar-on-matrix regression which incorporates nuclear and $\ell_1$ norm penalties to enforce simultaneously low-rank and sparse structures in the estimated coefficient…

  2. arXiv stat.ML TIER_1 English(EN) · Jaroslaw Harezlak ·

    用于脑成像的带联合核和 $\ell_1$ 正则化的逻辑矩阵回归

    We introduce a new convex optimization framework for logistic scalar-on-matrix regression which incorporates nuclear and $\ell_1$ norm penalties to enforce simultaneously low-rank and sparse structures in the estimated coefficient matrix. The proposed method enables interpretable…

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