(This is a work in progress. Feedback is welcome) An analytical comparison is made between slow feature analysis (SFA) and the successor representation (SR). While SFA and the SR stem from distinct areas of machine learning, they share important properties, both in terms of their mathematics and the types of information they are sensitive to. This work studies their connection along these two axes. In particular, multiple variants of the SFA algorithm are explored analytically and then applied to the setting of an MDP, leading to a family of eigenvalue problems involving the SR and other related quantities. These resulting eigenvalue problems are then illustrated in the toy setting of a gridworld, where it is demonstrated that the place- and grid-like fields often associated to the SR can equally be generated using SFA.

翻译：（本文为研究进展报告，欢迎反馈）本文对慢特征分析（SFA）与后继表示法（SR）进行了理论比较。尽管SFA和SR源于机器学习的不同领域，但它们在数学特性与信息敏感度方面具有重要共性。本研究从这两个维度系统探讨二者的内在联系。具体而言，本文通过理论分析探讨了SFA算法的多种变体，并将其应用于马尔可夫决策过程环境，推导出涉及SR及相关量的特征值问题族。随后在网格世界玩具环境中对这些特征值问题进行可视化验证，结果表明通常与SR相关的空间位置场与网格状场同样可通过SFA生成。