A maximum likelihood algorithm for slow features analysis
• Motivate temporal slowness as a useful learning principle• Introduce Slow Features Analysis (SFA) as a learning algorithm, based on thisprinciple and give some intuitions• We show how the SFA algorithm corresponds to maximum likelihood learningin a limit of a probabilistic model and illustrate with simulations• Suggest several extensions and improvements to the probabilistic modeland connect to other work.