The kernel of graph indices for vector search

arXiv:2506.20584 · cs.LG · Submitted 2025-06-25 · Read on arXiv

cs.LG

Submitted: 2025-06-25

Updated: 2026-09-24

Code: https://github.com/marianotepper/svg

Terminology

• Pruning — 14× in this paper · explained in 4 episodes
• Sparsity — 13× in this paper · explained in 4 episodes
• Graph Construction — 9× in this paper · explained in 3 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Support Vector Graph — 7× in this paper
• Clustering — 3× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reproducing Kernel Hilbert Space — 1× in this paper
• Reproducing Kernel Hilbert Space (RKHS) — 1× in this paper · explained in 3 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes

Sources

• A Survey on Locality Sensitive Hashing Algorithms and their Applications
• All Random Features Representations are Equivalent
• Neighborhood and Graph Constructions using Non-Negative Kernel Regression
• Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization
• Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

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