Designing unsupervised mixed‐type feature selection techniques using the heterogeneous correlation matrix
提出两种基于异质相关矩阵的无监督特征选择方法，用于处理同时包含数值和有序分类特征的数据，通过迭代移除冗余特征并利用p值矩阵确定特征数量。
Abstract Real‐life data often include both numerical and categorical features. When categorical features are ordinal, the Pearson correlation matrix (CM) can be extended to a heterogeneous CM (HCM), which combines Pearson's correlations (numerical‐numerical), polyserial correlations (numerical‐ordinal) and polychoric correlations (ordinal‐ordinal). HCM entries are comparable, enabling assessment of pairwise‐linear dependencies. An added benefit is the computation of ‐values for pairwise uncorrelation tests, forming a heterogeneous ‐values matrix (HPM). While the HCM has been used for unsupervised feature extraction (UFE), that is, transforming features into informative representations (e.g., PCA), its application to unsupervised feature selection (UFS), that is, selecting relevant features, remains unexplored. This paper proposes two HCM‐based UFS methods for mixed‐type features. These, called UFS‐rHCM and UFS‐cHCM, iteratively remove redundant features using the HCM—row‐wise (UFS‐rHCM) or cell‐wise (UFS‐cHCM). The HPM determines the stopping point, enabling a statistically grounded approach to selecting the number of features. We also introduce a visualization tool for assessing feature importance and ranking. The performance of our methods is evaluated on simulated and real datasets.