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2018 ICML ICML 2018

Streaming Principal Component Analysis in Noisy Setting

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.

🧭 Keyword Pioneer — noisy setting
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio

Authors

Teodor Vanislavov Marinov , Poorya Mianjy , Raman Arora

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Core Methods > Dimensionality Reduction Machine Learning > Learning Types > Online Learning Machine Learning > Optimization & Theory > Online Algorithms

Keywords

principal component analysis outlier detection sub-linear regret streaming principal component analysis streaming algorithm missing datum noisy setting
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