论文标题
降低高维分类数据的尺寸，并考虑了两个或多个响应，考虑了响应之间的相互作用
Dimension reduction of high-dimension categorical data with two or multiple responses considering interactions between responses
论文作者
论文摘要
本文将具有两个或多个响应的分类数据对数据进行分类，重点是响应之间的相互作用。我们提出了一个基于足够尺寸降低的有效迭代程序。我们研究了在两种响应模型和多响应模型下提出方法的理论保证，这证明了所提出的估计器的独特性，并且提出的方法恢复了Oracle最小二乘估计器的可能性很高。对于数据分析，我们证明了所提出的方法在多响应模型中有效，并且比在多响应模型中构建的一些现有方法更好。我们将此建模和提出的方法应用于成人数据集和正确的心脏导管数据集并获得有意义的结果。
This paper models categorical data with two or multiple responses, focusing on the interactions between responses. We propose an efficient iterative procedure based on sufficient dimension reduction. We study the theoretical guarantees of the proposed method under the two- and multiple-response models, demonstrating the uniqueness of the proposed estimator and with the high probability that the proposed method recovers the oracle least squares estimators. For data analysis, we demonstrate that the proposed method is efficient in the multiple-response model and performs better than some existing methods built in the multiple-response models. We apply this modeling and the proposed method to an adult dataset and right heart catheterization dataset and obtain meaningful results.
