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- W2891362039 abstract "High dimensional data means that the number of variables p if far largerthan the number of observations n.This talk starts from a survey of various solutions in linear regression .When p n the OLS estimator does not exist . Since it is a case of forced multi-collinearity, one may use regularized techniques such as ridge regression, principalcomponent regression or PLS regression which keep all the predictors.However if p n combinations of all variables cannot be interpreted. Sparse so-lutions, ie with a large number of zero coe cients, are preferred. Lasso, elastic net,sparse PLS perform simultaneously regularization and variable selection thanks tonon quadratic penalties: L1, SCAD etc.In PCA, the singular value decomposition shows that if we regress principal com-ponents onto the input variables, the vector of regression coe cients is equal to thefactor loadings. It su ces to adapt sparse regression techniques to get sparse ver-sions of PCA and of PCA with groups of variables. We conclude by a presentation ofa sparse version of Multiple Correspondence Analysis and give several applications." @default.
- W2891362039 created "2018-09-27" @default.
- W2891362039 creator A5019375557 @default.
- W2891362039 date "2013-07-10" @default.
- W2891362039 modified "2023-10-04" @default.
- W2891362039 title "From Sparse Regression to Sparse Multiple Correspondence Analysis" @default.
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