Neural Comput - On the relation of slow feature analysis and Laplacian eigenmaps.

Tópicos

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Resumo

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.

Resumo Limpo

past decad seen rise interest laplacian eigenmap lem nonlinear dimension reduct lem use spectral cluster semisupervis learn provid effici state represent reinforc learn show lem close relat slow featur analysi sfa biolog inspir unsupervis learn algorithm origin design learn invari visual represent show sfa can interpret function approxim lem topolog neighborhood requir lem implicit defin tempor structur data base relat propos general sfa arbitrari neighborhood relat demonstr applic spectral cluster final review previous work goal provid unifi view sfa lem

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