Streaming Principal Component Analysis in Noisy Setting

ICML 2018 · Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora ·

We study streaming algorithms for principal component analysis (PCA) in noisy settings. We present computationally efficient algorithms with sub-linear regret bounds for PCA in the presence of noise, missing data, and gross outliers.

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