Robust Stochastic Principal Component Analysis

  title={Robust Stochastic Principal Component Analysis},
  author={John Goes and Teng Zhang and Raman Arora and Gilad Lerman},
We consider the problem of finding lower dimensional subspaces in the presence of outliers and noise in the online setting. In particular, we extend previous batch formulations of robust PCA to the stochastic setting with minimal storage requirements and runtime complexity. We introduce three novel stochastic approximation algorithms for robust PCA that are extensions of standard algorithms for PCA – the stochastic power method, incremental PCA and online PCA using matrix-exponentiated-gradient… CONTINUE READING
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