Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. LDA in the binaryclass case has been shown to be equivalent to linear re...
Abstract-- We consider optimal experiment design for parametric prediction error system identification of linear timeinvariant systems in closed loop. The optimisation is performed...
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
Abstract--High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is ...
The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this p...