  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.
