Slow Feature Analysis as Variational Inference Objective

Developing probabilistic perspectives on established mac hine learning algorithms can be a promising endeavor, as it casts methods originating from, for example, geometric or h euristic concepts into a well-understood framework that allows one to make explicit the assumptions and the dependen cies that are inherent in the resulting model. Many methods have been described in this shared language, even spanni ng the broad machine learning paradigms of unsupervised, supervised, and reinforcement learning. This makes it poss ible to compare methods, understand shortcomings, and propose extensions through a rich body of broad research. Furthermore, previous research on a specific method that was generalized in such a way might prove to be useful for the field of probabilistic modeling itself. After all, the mo st efficient methods for probabilistic inference under a mod el are rarely the most general and often leverage the model-spe cific structure (Kalman, 1960; Margossian & Blei, 2024). In this work, a soft variant of Slow Feature Analysis (SFA) (W iskott, 1998; Wiskott & Sejnowski, 2002) is derived using the language of probabilistic inference.