Slow Feature Analysis as Variational Inference Objective

• URL: http://arxiv.org/abs/2506.00580v1
• Date: Sat, 31 May 2025 14:29:02 GMT
• Title: Slow Feature Analysis as Variational Inference Objective
• Abstract summary: This work presents a novel probabilistic interpretation of Slow Feature Analysis (SFA) through the lens of variational inference.<n>It recasts the classical slowness objective in a variational framework.<n>We provide arguments, why -- from the perspective of slowness optimization -- the reconstruction loss takes on the role of the constraints that ensure informativeness in SFA.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a novel probabilistic interpretation of Slow Feature Analysis (SFA) through the lens of variational inference. Unlike prior formulations that recover linear SFA from Gaussian state-space models with linear emissions, this approach relaxes the key constraint of linearity. While it does not lead to full equivalence to non-linear SFA, it recasts the classical slowness objective in a variational framework. Specifically, it allows the slowness objective to be interpreted as a regularizer to a reconstruction loss. Furthermore, we provide arguments, why -- from the perspective of slowness optimization -- the reconstruction loss takes on the role of the constraints that ensure informativeness in SFA. We conclude with a discussion of potential new research directions.

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