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• W2794301818 abstract "The computational principles of slowness and predictability have been proposed to describe aspects of information processing in the visual system. From the perspective of slowness being a limited special case of predictability we investigate the relationship between these two principles empirically. On a collection of real-world data sets we compare the features extracted by slow feature analysis (SFA) to the features of three recently proposed methods for predictable feature extraction: forecastable component analysis, predictable feature analysis, and graph-based predictable feature analysis. Our experiments show that the predictability of the learned features is highly correlated, and, thus, SFA appears to effectively implement a method for extracting predictable features according to different measures of predictability." @default.
• W2794301818 created "2018-03-29" @default.
• W2794301818 creator A5039663126 @default.
• W2794301818 creator A5041541630 @default.
• W2794301818 date "2018-05-01" @default.
• W2794301818 modified "2023-09-24" @default.
• W2794301818 title "Slowness as a Proxy for Temporal Predictability: An Empirical Comparison" @default.
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• W2794301818 doi "https://doi.org/10.1162/neco_a_01070" @default.
• W2794301818 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29566353" @default.
• W2794301818 hasPublicationYear "2018" @default.
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