Cheolmin Kim

Stochastic Variance-Reduced Algorithms for PCA with Arbitrary Mini-Batch Sizes

We present two stochastic variance-reduced PCA algorithms and their convergence analyses. By deriving explicit forms of step size, epoch length and batch size to ensure the optimal runtime, we show that the proposed algorithms can attain the optimal runtime with any batch sizes. Also, we establish g…