Abstract:
:Temporal slowness is a learning principle that allows learning of invariant representations by extracting slowly varying features from quickly varying input signals. Slow feature analysis (SFA) is an efficient algorithm based on this principle and has been applied to the learning of translation, scale, and other invariances in a simple model of the visual system. Here, a theoretical analysis of the optimization problem solved by SFA is presented, which provides a deeper understanding of the simulation results obtained in previous studies.
journal_name
Neural Computjournal_title
Neural computationauthors
Wiskott Ldoi
10.1162/089976603322297331subject
Has Abstractpub_date
2003-09-01 00:00:00pages
2147-77issue
9eissn
0899-7667issn
1530-888Xjournal_volume
15pub_type
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