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A maximum-likelihood interpretation for slow feature analysis

Turner, R; Sahani, M; (2007) A maximum-likelihood interpretation for slow feature analysis. NEURAL COMPUT , 19 (4) 1022 - 1038.

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Abstract

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses "slowness" as a heuristic by which to extract semantic information from multidimensional time series. Here, we develop a probabilistic interpretation of this algorithm, showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual springboard with which to motivate several novel extensions to the algorithm.

Type:Article
Title:A maximum-likelihood interpretation for slow feature analysis
Keywords:NATURAL IMAGE SEQUENCES, COMPLEX CELL PROPERTIES, COMPONENT ANALYSIS, SOURCE SEPARATION, MODELS, STATISTICS, STIMULI
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit

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