Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Slow feature analysis: a theoretical analysis of optimal free responses
Neural Computation
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
A tutorial on spectral clustering
Statistics and Computing
Graph Laplacians and their Convergence on Random Neighborhood Graphs
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Invariant Object Recognition with Slow Feature Analysis
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Towards a theoretical foundation for Laplacian-based manifold methods
Journal of Computer and System Sciences
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Construction of approximation spaces for reinforcement learning
The Journal of Machine Learning Research
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The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs.