Published on Tue Apr 03 2018

Average performance analysis of the stochastic gradient method for online PCA

Stephane Chretien, Christophe Guyeux, Zhen-Wai Olivier HO

This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting the learning rate.

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Abstract

This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting the learning rate. Simulation experiments confirm the practical relevance of the plain stochastic gradient approach and that drastic improvements can be achieved by learning the learning rate.