Two PCA-based metrics, eigenvalue difference and first-eigenvector angle, are proposed for dataset similarity and shown to aid synthetic data and feature selection evaluation.
A kernel two-sample test
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Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version
Two PCA-based metrics, eigenvalue difference and first-eigenvector angle, are proposed for dataset similarity and shown to aid synthetic data and feature selection evaluation.