Statistics Seminar: Koch -- Classification and Prediction in Independent Component Regression

                Classification and Prediction 
           with Independent Component Regression

                        Inge Koch 
             (University of New South Wales)

          Friday, 31 August, 2007, 2pm Carslaw 373 

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For high-dimensional data the number of variables needs to be
reduced before conventional classification and regression techniques
can be applied. Principal Component Regression selects a reduced
number of predictors from the original variables, but these
predictors can be unrelated to the outcome variables, as they are
chosen merely by their contribution to variance. We propose a method
which combines variable ranking with a selection of the best reduced
subset of predictors. Variable ranking is achieved by canonical
correlation analysis, and the selection of the best subset is
accomplished with independent component analysis. The method is
applicable to classification and regression problems with
multivariate response variables. We demonstrate the performance of
the method on real data and simulation studies and show that it
compares favourably with recent supervised classification and
prediction techniques.
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