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Nyström Method with Kernel K-means++ Samples as Landmarks

ICML 2017 · Learning theory and algorithms › Clustering · approach: k-means++ seeding analysis · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use kernel K-means++ samples as landmarks instead of uniform, leverage score or K-DPP samplingNystrom low-rank approximation of Gaussian kernel matrix · 13 UCI/LIACC datasets, K=100, varying bandwidthlift of Frobenius norm errorHelped
Increase K-DPP chain length from 1 000 to 10 000 MC steps with uniform initial state (ablation)K-DPP Nystrom method · ailerons, parkinsons, elevators, ujil, cal-housinglift of Frobenius norm error: 1 000 -> 10 000 MC stepsHelped
Run K-DPP chain beyond 1 000 steps from a uniformly random initial state (ablation)K-DPP Nystrom method · five datasets, K=100runtime vs accuracyMixed
Initialize K-DPP chain with K-means++ samples instead of uniform sampling (ablation)K-DPP Nystrom method · Comparison with Li et al. (2016)runtime and accuracyHelped
Increase approximation rank from 5 to 100 (ablation)kernel K-means++ sampling · five datasets, median-distance bandwidthlift of Frobenius norm error: 5 -> 100Helped
Use approximate leverage score sampling instead of K-DPP initialized uniformlylandmark selection baselines · five datasets, rank sweeplift of Frobenius norm errorHelped
Select l = K ln n landmarks instead of l = K ln K for a rank K=100 approximation (ablation)kernel K-means++ sampling · Appendix C, K=100lift of Frobenius norm errorNo clear effect
Select l = K ln K landmarks instead of l = K for a rank K approximation (ablation)kernel K-means++ sampling · K=100approximation accuracy and selection timeHelped
Use kernel K-means++ sampling instead of approximate leverage score sampling on high-dimensional datalandmark selection · ujil (more than 500 attributes)selection timeHurt
Use leverage score sampling instead of kernel K-means++ on cal-housinglandmark selection · cal-housinglift of Frobenius norm errorHurt

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