Research map / Learning theory and algorithms / Matrix and tensor methods
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the KDE-based low-rank approximation algorithm instead of the baselines | Kernel matrices · Low-rank approximation (empirical) | kernel evaluations: 9x decrease | Helped |
| Use the KDE-based spectral sparsification algorithm instead of the baselines | Kernel graphs · Spectral sparsification (empirical) | graph size: 41x reduction | Helped |
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