63 citations · 66 across the 4 of their papers we have counts for
4 papers
Stochastic Scale Invariant Power Iteration for KL-divergence Nonnegative Matrix Factorization
Cheolmin Kim, Youngseok Kim, Diego Klabjan
We introduce a mini-batch stochastic variance-reduced algorithm to solve finite-sum scale invariant problems which cover several examples in machine learning and statistics such as…
Scale Invariant Power Iteration
Cheolmin Kim, Youngseok Kim, Diego Klabjan
Power iteration has been generalized to solve many interesting problems in machine learning and statistics. Despite its striking success, theoretical understanding of when and how…
Stochastic Variance-Reduced Heavy Ball Power Iteration
Cheolmin Kim, Diego Klabjan
We present a stochastic variance-reduced heavy ball power iteration algorithm for solving PCA and provide a convergence analysis for it. The algorithm is an extension of heavy ball…
A Simple and Fast Algorithm for L1-norm Kernel PCA
Cheolmin Kim, Diego Klabjan
We present an algorithm for L1-norm kernel PCA and provide a convergence analysis for it. While an optimal solution of L2-norm kernel PCA can be obtained through matrix decompositi…