处理特征数据缺失的条件多维缩放方法，提升降维精度与可解释性。
Conditional Multidimensional Scaling with Incomplete Conditioning Data
- 基于不完整已知特征，联合学习低维配置与缺失值补全
- 在特征缺失场景下仍能保持高精度的降维结果
- 适合数据不完整时的降维分析，尤其适用于生物或社会科学研究
条件多维缩放旨在利用已知特征和成对差异信息，求解低维配置。现有方法依赖完整的已知特征数据，但实际中常因时间、成本等原因导致数据缺失。本文提出一种可在已知特征存在缺失值时进行低维配置学习的方法，不仅能实现降维，还可同时补全缺失特征，提供额外洞察。该方法的 R 语言实现已开源，托管于 CRAN 的 cml 包中。
原文摘要 · Abstract (English)
Conditional multidimensional scaling seeks for a low-dimensional configuration from pairwise dissimilarities, in the presence of other known features. By taking advantage of available data of the known features, conditional multidimensional scaling improves the estimation quality of the low-dimensional configuration and simplifies knowledge discovery tasks. However, existing conditional multidimensional scaling methods require full data of the known features, which may not be always attainable due to time, cost, and other constraints. This paper proposes a conditional multidimensional scaling method that can learn the low-dimensional configuration when there are missing values in the known features. The method can also impute the missing values, which provides additional insights of the problem. Computer codes of this method are maintained in the cml R package on CRAN.
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