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We study PCA as a stochastic optimization problem and propose a novelstochastic approximation algorithm which we refer to as "Matrix StochasticGradient" (MSG), as well as a practical variant, Capped MSG. We study themethod both theoretically and empirically.

Stochastic Optimization of PCA with Capped MSG