In the article The Journal of Neuroscience, March 14, 2018 • 38(11):2755–2765 • 2755, the authors do not use the classical SVM classifier : "Each analysis used a shrinkage discriminant analysis (SDA) as a classifier, a form of regularized linear discriminant analysis that can be applied to high-dimensional data that have more variables (voxels) than observations (trials). With this method, the estimates of the category means and covariances are shrunk toward zero using James–Stein shrinkage estimators as a way to ensure the estimability of the inverse covariance matrix and to reduce the mean squared error when used for out-of-sample prediction (SDA package for R; Ahdesmäki et al., 2014)."
Is this shrinkage discriminant analysis (SDA) classifier implemented in nilearn ? Is it indeed more efficient / relevant than the usual SVM ?
In the article The Journal of Neuroscience, March 14, 2018 • 38(11):2755–2765 • 2755, the authors do not use the classical SVM classifier : "Each analysis used a shrinkage discriminant analysis (SDA) as a classifier, a form of regularized linear discriminant analysis that can be applied to high-dimensional data that have more variables (voxels) than observations (trials). With this method, the estimates of the category means and covariances are shrunk toward zero using James–Stein shrinkage estimators as a way to ensure the estimability of the inverse covariance matrix and to reduce the mean squared error when used for out-of-sample prediction (SDA package for R; Ahdesmäki et al., 2014)."
Is this shrinkage discriminant analysis (SDA) classifier implemented in nilearn ? Is it indeed more efficient / relevant than the usual SVM ?