SPEC finds and aligns the sample clusters that two embedding models capture differently by analyzing the eigenvectors of the difference of their kernel matrices.
Spectral clustering and kernel PCA are learning eigenfunctions , volume 1239
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Towards an Explainable Comparison and Alignment of Feature Embeddings
SPEC finds and aligns the sample clusters that two embedding models capture differently by analyzing the eigenvectors of the difference of their kernel matrices.