Understanding Slow Feature Analysis: A Mathematical Framework
نویسندگان
چکیده
منابع مشابه
Understanding Slow Feature Analysis: A Mathematical Framework
Slow feature analysis is an algorithm for unsupervised learning of invariant representations from data with temporal correlations. Here, we present a mathematical analysis of slow feature analysis for the case where the input-output functions are not restricted in complexity. We show that the optimal functions obey a partial differential eigenvalue problem of a type that is common in theoretica...
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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...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2008
ISSN: 1556-5068
DOI: 10.2139/ssrn.3076122