Various techniques, used to optimize on-line principal component analysis, are investigated by methods of statistical mechanics. These include local and global optimization of node-dependent learning-rates which are shown to be very efficient in speeding up the learning process. They are investigated further for gaining insight into the learning rates' time-dependence, which is then employed for devising simple practical methods to improve training performance. Simulations demonstrate the benefit gained from using the new methods.

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