Kernel Conjugate Gradient Methods with Random Projections

5 Nov 2018Junhong LinVolkan Cevher

We propose and study kernel conjugate gradient methods (KCGM) with random projections for least-squares regression over a separable Hilbert space. Considering two types of random projections generated by randomized sketches and Nystr\"{o}m subsampling, we prove optimal statistical results with respect to variants of norms for the algorithms under a suitable stopping rule... (read more)

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