PCA is good, but you could also try playing around with Sparse (robust) PCA. The sparsification loses orthogonality, but does not necessarily lose information, it can yield a different rotation and cleaner vectors. Now whether that matters in the context of LLMs/Embeddings - I cannot tell.
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PCA is good, but you could also try playing around with Sparse (robust) PCA. The sparsification loses orthogonality, but does not necessarily lose information, it can yield a different rotation and cleaner vectors. Now whether that matters in the context of LLMs/Embeddings - I cannot tell.