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arXiv Machine Learning By Mariano Tepper, Ted Willke

The kernel of graph indices for vector search

Read the original on arXiv Machine Learning →

The paper introduces the Support Vector Graph (SVG), a graph index for vector search that uses kernel methods to guarantee navigability in both metric and non‑metric vector spaces, such as inner product similarity. It shows that popular indices like HNSW and DiskANN are special cases of SVG, and proposes SVG‑L0, which adds an ℓ₀ sparsity constraint to enforce bounded out‑degree while maintaining computational efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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