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Neighborhood Components Analysis for Reward-Based Dimensionality Reduction

posted Sep 2, 2008, 10:22 AM by Brian Tanner   [ updated Sep 2, 2008, 1:23 PM ]

Nathan Sprague


There has been a great deal of research that attempts to explain the structure of biological receptive fields in terms of various methods for adapting basis vectors based on the statistical structure of visual input.  These include principal components analysis (Hancock et al., 1992), independent components analysis (Bell & Sejnowski, 1997), non-negative matrix factorization (Lee & Seung, 1999), and predictive coding (Rao & Ballard, 1999), among others. Typically, such approaches are based purely on the structure of the visual input; there is no consideration of the role that visual information plays in the goal directed behavior of an organism. The motivation for the current work is to explore mechanisms of basis vector adaptation that are explicitly driven by the behavioral demands of a situated agent.

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