Topographic Independent Component Analysis
| Autor: | Aapo Hyvärinen, Mika Inki, Patrik O. Hoyer |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Analysis of Variance
Brain Mapping Blinking Cognitive Neuroscience Models Neurological Mathematical analysis Structure (category theory) Magnetoencephalography Residual Independent component analysis Blind signal separation Arts and Humanities (miscellaneous) Principal component analysis Statistics Humans Mastication Artifacts Likelihood function Representation (mathematics) Algorithms Linear filter Muscle Contraction Mathematics |
| Zdroj: | Encyclopedia of Computational Neuroscience |
| ISSN: | 1530-888X 0899-7667 |
| DOI: | 10.1162/089976601750264992 |
| Popis: | In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated “independent” components are often not at all independent. We propose that this residual dependence structure could be used to define a topo-graphic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation. |
| Databáze: | OpenAIRE |
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