[r/ML] [P] PCA before truncation makes non-Matryoshka embeddings compressible: results on BGE-M3 [P]

Summary

This news presents a simple yet effective method to compress non-Matryoshka embedding models, which usually degrade significantly with naive dimension truncation. By applying Principal Component Analysis (PCA) to embeddings first, the signal is concentrated into leading components, enabling high-quality truncation. Tests on BGE-M3 embeddings demonstrated substantial improvements in cosine similarity, making them much more compressible for practical use.

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