Online PCA with Optimal Regrets.

Journal of Machine Learning Research, (2016): 173:1-173:49

Cited by: 1|Views125
EI

Abstract

We investigate the online version of Principle Component Analysis (PCA), where in each trial t the learning algorithm chooses a k-dimensional subspace, and upon receiving the next instance vector xt, suffers the loss, which is the squared Euclidean distance between this instance and its projection into the chosen subspace. When viewed in...More

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