Presentation - MIV

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collet@lsiit.u-strasbg.fr iAstro Workshop - Nice Observatory 16/17 October 2003 Classification Space spanned by feature vectors * is subdivided using decision boundaries * which are established by statistical decision theory * Bayes decision theory : average risk is minimized Performances of a classifier * sample size * nb of features * classifier complexity (criterion function) Classification of high dimensional vector * curse of dimensionality * main factor affecting the classification task
collet@lsiit.u-strasbg.fr iAstro Workshop - Nice Observatory 16/17 October 2003 Hughe phenomenon Inherent sparsity of high dimensional spaces * in the absence of simplifying assumptions, the amount of data needed to get reasonably low variance estimators is really high * N-band observations >> N times more data but in R N space Dimensionality reduction * appropriate dimensionality of the reduced feature space * Important structure in the data actually lies in a much smaller dimensional space, and will therefore try to reduce the dimensionality before attempting the classification. This approach can be successful if the dimensionality reduction/feature extraction method loses as little relevant information as possible in the transformation from high-dimensional space to the low-dimensional one.
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Presentation - MIV

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