In this project it is used a Machine Learning model based on a method called Extreme Learning, with the employment of L2-regularization. In particular, a comparison was carried out between: (A1) which ...

with being the (tall thin) matrix from the ML-cup dataset by Prof. Micheli, , and is a random vector. (A1) is thin QR factorization via Householder reflectors, cf. also Lecture 10. (A2) is a variant ...

Abstract: We consider computing the QR factorization with column pivoting (QRCP) for a tall and skinny matrix, which has important applications including low-rank approximation and rank determination.