多层次奇异值分解处理混合数据插补数据集
多医院创伤患者混合数据聚合集，用于缺失值插补方法研究。
基本信息
资源简介
多层次奇异值分解处理混合数据插补数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 1.29 MB，主题涵盖 Distributed computation、Hierarchical data、Low-rank matrix estimation、Matrix completion，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Imputation of Mixed Data With Multilevel Singular Value Decomposition》。配套论文《Imputation of Mixed Data With Multilevel Singular Value Decomposition》发表于 Journal of Computational and Graphical Statistics（2019），DOI：10.1080/10618600.2019.1585261。项目资助方：Geneviève Robin was funded by the France-Stanford Center for Interdisciplinary Studies (visiting student researcher fellowship) to visit Stanford.。源站首次发布：2019-10-25。
【数据内容】
源站原始描述：Statistical analysis of large datasets offers new opportunities to better understand underlying processes. Yet, data accumulation often implies relaxing acquisition procedures or compounding diverse sources. As a consequence, datasets often contain mixed data, that is, both quantitative and qualitative, and many missing values. Furthermore, aggregated data present a natural multilevel structure, where individuals or samples are nested within different sites, such as countries or hospitals. Imput…
中文译文（机器翻译，仅供快速理解，请以原文为准）：大型数据集的统计分析为更好地理解潜在过程提供了新机会。然而，数据积累往往意味着放宽采集程序或合并不同来源。因此，数据集常常包含混合数据，即同时包含定量和定性数据，以及大量缺失值。此外，聚合数据呈现出自然的多层结构，其中个体或样本嵌套于不同站点，例如国家或医院。因此，多层数据的插补近年来引起了一些关注，但当前解决方案并非为处理混合数据而设计，并且存在重要缺陷，例如计算成本高。在本文中，我们提出一种用于多层数据的单一插补方法，可用于补全定量、分类或混合数据。该方法基于多层奇异值分解（SVD），即将数据的变异性分解为两个成分，
【数据结构与技术规格】
文件数 1 个，合计 1.29 MB。ucgs_a_1585261_sm7827.zip：application/zip，1.29 MB。数据集 DOI：10.6084/m9.figshare.7827983.v1。
【主题与分类】
源站学科分类：Genetics、Chemical Sciences not elsewhere classified、Immunology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：Distributed computation、Hierarchical data、Low-rank matrix estimation、Matrix completion。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Imputation_of_Mixed_Data_With_Multilevel_Singular_Value_Decomposition/7827983
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Imputation of Mixed Data With Multilevel Singular Value Decomposition. Taylor & Francis. Husson, François; Josse, Julie; Narasimhan, Balasubramanian; Robin, Geneviève
【使用注意】
· 源站最后修改于 2023-05-31，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Imputation of Mixed Data With Multilevel Singular Value Decomposition
- DOI：10.6084/m9.figshare.7827983.v1
- 发布日期：2019-10-25
- 文件数：1 个
- 文件总体积：1.29 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/Imputation_of_Mixed_Data_With_Multilevel_Singular_Value_Decomposition/7827983
命令行下载
curl -L -o "ucgs_a_1585261_sm7827.zip" "https://ndownloader.figshare.com/files/18112988"
文件清单
ucgs_a_1585261_sm7827.zip（1.29 MB）
文件总体积 1.29 MB（源站 API 实测）。
数据说明
Statistical analysis of large datasets offers new opportunities to better understand underlying processes. Yet, data accumulation often implies relaxing acquisition procedures or compounding diverse sources. As a consequence, datasets often contain mixed data, that is, both quantitative and qualitative, and many missing values. Furthermore, aggregated data present a natural multilevel structure, where individuals or samples are nested within different sites, such as countries or hospitals. Imputation of multilevel data has therefore drawn some attention recently, but current solutions are not designed to handle mixed data, and suffer from important drawbacks, such as their computational cost. In this article, we propose a single imputation method for multilevel data, which can be used to complete either quantitative, categorical, or mixed data. The method is based on multilevel singular value decomposition (SVD), which consists in decomposing the variability of the data into two components, the between and within groups variability, and performing an SVD on both parts. We show on a simulation study that in comparison to competitors, the method has the advantages of handling datasets of various size, and being computationally faster. Furthermore, it is the first so far to handle mixed data. We apply the method to impute a medical dataset resulting from the aggregation of several hospitals datasets. This application falls in the framework of a larger project on Trauma patients. To overcome obstacles associated to the aggregation of medical data, we turn to distributed computation. The method is implemented in the R package missMDA. Supplementary materials for this article are available online.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.7827983.v1（source: https://tandf.figshare.com/articles/dataset/Imputation_of_Mixed_Data_With_Multilevel_Singular_Value_Decomposition/7827983）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
