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Streaming Principal Component Analysis in Noisy Setting
Teodor Vanislavov Marinov · Poorya Mianjy · Raman Arora

Wed Jul 11 05:20 AM -- 05:30 AM (PDT) @ K11

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.

Author Information

Teodor Vanislavov Marinov (Johns Hopkins University)
Poorya Mianjy (Johns Hopkins University)
Raman Arora (Johns Hopkins University)
Raman Arora

Raman Arora received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. From 2009-2011, he was a Postdoctoral Research Associate at the University of Washington in Seattle and a Visiting Researcher at Microsoft Research Redmond. Since 2011, he has been with Toyota Technological Institute at Chicago (TTIC). His research interests include machine learning, speech recognition and statistical signal processing.

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