We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings. Previous literature has focused on upper bounding the static adversarial regret, whose comparator is the optimal fixed action in hindsight. However, static regret is not an appropriate metric when the underlying environment is changing. Instead, we adopt the adaptive regret metric from the previous literature and propose online adaptive algorithms for PCA and variance minimization, that have sub-linear adaptive regret guarantees. We demonstrate both theoretically and experimentally that the proposed algorithms can adapt to the changing environments.
Jianjun Yuan (University of Minnesota)
Andrew Lamperski (University of Minnesota)
Related Events (a corresponding poster, oral, or spotlight)
2019 Oral: Online Adaptive Principal Component Analysis and Its extensions »
Thu Jun 13th 11:30 -- 11:35 AM Room Room 102