We propose a new method to impute missing values in mixed datasets. It is based on a principal components method, the factorial analysis for mixed data, which balances the influence of all the variables that are continuous and categorical in the construction of the dimensions of variability. Because the imputation uses the principal axes and components, the prediction of the missing values are based on the similarity between individuals and on the relationships between variables. The quality of the imputation is assessed through a simulation study and real datasets. The method is compared to a recent method (Stekhoven and Bühlmann, 2011) based on random forests and shows better performances especially for the imputation of categorical variables and when there are highly linear relationships between continuous variables.
Missing data imputation for noisy time-series data and applications in healthcare
Faculty of Mathematics and Computer Science, University of Science（胡志明市科学大学数学与计算机科学学院）；Vietnam National University（越南国家大学）；University of Medicine and Pharmacy at Ho Chi Minh City（胡志明市医药大学）；Simula Metropolitan（西穆拉都会研究院）；AISIA Research Laboratory（AISIA研究院）
Biomedical Research and Innovation Institute of Cádiz (INiBICA)（加的斯生物医学研究与创新研究所（INiBICA））；Puerta del Mar University（马门大学）；Universidad Autónoma de Madrid（马德里自治大学）；Puerta del Mar University Hospital（马门大学医院）；University of Cádiz（加的斯大学）
Extended Missing Data Imputation via GANs for Ranking Applications
Cornell University（康奈尔大学）；Amazon（亚马逊）
我们提出了条件填补GAN，一种基于生成对抗网络（GANs）的扩展缺失数据填补方法。激励用例是学习排名，现代搜索，推荐系统和信息检索应用程序的基石。经验排序数据集并不总是遵循标准高斯分布或完全随机缺失（MCAR）机制，这是经典缺失数据插补方法的标准假设。我们的方法提供了一个简单的解决方案，提供兼容的插补保证，同时放松假设缺失的机制和回避近似棘手的分布，以提高插补质量。我们证明了最佳的GAN填补实现扩展随机缺失（EMAR）和扩展总是随机缺失（EAMAR）机制，超越了天真的MCAR。我们的方法在开源Microsoft Research Ranking（MSR）数据集和合成排名数据集上展示了最高的插补质量，与最先进的基准和各种特征分布相比。使用专有的Amazon Search排名数据集，我们还展示了与地面实况数据相比，在GAN估算数据上训练的排名模型的可比排名质量指标。
