SPEC finds and aligns the sample clusters that two embedding models capture differently by analyzing the eigenvectors of the difference of their kernel matrices.
Learning eigenfunctions of similarity: linking spectral clustering and kernel PCA
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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.