Abstract:
: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.
journal_name
Neural Computjournal_title
Neural computationauthors
Sprekeler Hdoi
10.1162/NECO_a_00214subject
Has Abstractpub_date
2011-12-01 00:00:00pages
3287-302issue
12eissn
0899-7667issn
1530-888Xjournal_volume
23pub_type
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