Streaming Principal Component Analysis in Noisy Setting
Teodor Vanislavov Marinov ⋅ Poorya Mianjy ⋅ Raman Arora
2018 Poster
Abstract
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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