Stochastic Optimization of PCA with Capped MSG
arXiv:1307.1674v186 citations
Originality Incremental advance
AI Analysis
This work addresses PCA optimization for data analysis, but appears incremental as it builds on existing stochastic methods.
The authors tackled PCA as a stochastic optimization problem by proposing a novel stochastic approximation algorithm called Matrix Stochastic Gradient (MSG) and a practical variant, Capped MSG, studying it theoretically and empirically.
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.