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English(EN) Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data

新的 Tri-SfSVD 方法揭示复杂纵向数据中的模式

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 Tri-SfSVD，这是一种新颖的稀疏函数奇异值分解框架，旨在揭示复杂纵向数据中的模式。该方法直接分析观测数据，将连续轨迹估计与主体、特征和时间间隔的同时选择相结合。Tri-SfSVD 旨在通过避免临时插补和限制性形状假设来克服现有方法的局限性，从而能够在多个层面发现局部结构。 AI

影响 为分析复杂纵向数据引入了新的统计框架，有可能改善医学和生物学研究中的亚型识别。

排序理由 该集群包含一篇详细介绍新数据分析统计方法的学术论文。

在 arXiv stat.ML 阅读 →

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新的 Tri-SfSVD 方法揭示复杂纵向数据中的模式

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yue Zhao, Thierry Chekouo, Sandra Safo ·

    稀疏函数奇异值分解用于双聚类和三聚类纵向数据

    arXiv:2606.05488v1 Announce Type: new Abstract: Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data. However, these data are typically high-dimensional, sparsely sampled, and irr…

  2. arXiv stat.ML TIER_1 English(EN) · Sandra Safo ·

    稀疏函数奇异值分解用于双聚类和三聚类纵向数据

    Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data. However, these data are typically high-dimensional, sparsely sampled, and irregularly observed over time, posing substantial …

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