Stochastic Optimization of PCA with Capped MSG
Raman Arora ⋅ Andrew Cotter ⋅ Nati Srebro
2013 Poster
Abstract
We study PCA as a stochastic optimization problem and propose a novel stochastic approximation algorithm which we refer to as "Matrix Stochastic Gradient'' (MSG), as well as a practical variant, Capped MSG. We study the method both theoretically and empirically.
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