Research map / Learning theory and algorithms / Kernels and regression theory
Statistical and Computational Trade-Offs in Kernel K-Means
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the Nyström subspace size m | Nyström kernel k-means · MNIST60k | expected cost W(C_{n,m}^{++}, μ_test) | Helped |
| Increase the Nyström subspace size beyond the point where 1/m ≃ 1/√n | Nyström kernel k-means · MNIST60k | expected cost W(C_{n,m}^{++}, μ_test) | No clear effect |
| Increase the Nyström subspace size beyond the point where 1/m ≃ 1/√n | Nyström kernel k-means · MNIST60k | NMI | No clear effect |
| Use 400 uniformly sampled landmarks instead of 1600 | Nyström kernel k-means on MNIST8m · MNIST8m | NMI: 0.406 -> 0.405 | No clear effect |
| Use uniform landmark sampling rather than the prior PCA-based embedding with RLS sampling | Nyström kernel k-means on MNIST8m · MNIST8m | NMI: 0.423 -> 0.405 | Hurt |
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