We propose practical stopping criteria for the iterative solution of sparse linear least squares (LS) problems. Although we focus our discussion on the algorithm LSQR of Paige and ...
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...
In this paper we consider a regularization approach to variable selection when the regression function depends nonlinearly on a few input variables. The proposed method is based o...
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Ales...
In this paper, we propose a new algorithm, TLS-FOCUSS, for sparse recovery for large underdetermined linear systems, based on total least square (TLS) method and FOCUSS(FOCal Unde...
In this paper, a new kernel-based method for data visualization and dimensionality reduction is proposed. A reference point is considered corresponding to additional constraints ta...