Presentation - MIV

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collet@lsiit.u-strasbg.fr iAstro Workshop - Nice Observatory 16/17 October 2003 Dimensionality reduction Multidimensional scaling * multivariate data analysis techniques : any method searching for a low dimensional representation of objects given their high-dimensional representation Projection pursuit * Battacharya distance between 2 distributions * Subspaces max this distance Kohonen’s self organizing map
collet@lsiit.u-strasbg.fr iAstro Workshop - Nice Observatory 16/17 October 2003 Dimensionality reduction Limits A reduction in the number of features may lead to a loss in the discrimination power and thereby lower the accuracy of the resulting recognition system. Dimensionality reduction * feature selection : selects best subset of the input feature set * feature extraction : creates new features based on transformation or combination of the original feature The main issue in dimensionality reduction is the choice of a criterion function. A commonly used criterion is the classification error of a feature subset.
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Presentation - MIV

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