title: Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images.
abstract: Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.
authors.affiliation: Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland ; Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland.
authors.forename: Michael U
authors.initials: MU
authors.lastname: Gutmann
authors.affiliation: Image Processing Laboratory, Universitat de València, València, Spain.
authors.forename: Valero
authors.initials: V
authors.lastname: Laparra
authors.affiliation: Department of Computer Science, University of Helsinki, Helsinki, Finland ; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland ; Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland ; Cognitive Mechanisms Laboratories, ATR, Kyoto, Japan.
authors.forename: Aapo
authors.initials: A
authors.lastname: Hyvärinen
authors.affiliation: Image Processing Laboratory, Universitat de València, València, Spain.
authors.forename: Jesús
authors.initials: J
authors.lastname: Malo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003118:Color Perception / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008027:Light; D015233:Models, Statistical; D009488:Neurosciences / Q000379:methods; D010775:Photic Stimulation / Q000379:methods; D011336:Probability; D011601:Psychophysics; D014793:Visual Cortex / Q000502:physiology