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SIAMMAX
2010
92views more  SIAMMAX 2010»
13 years 3 months ago
Estimating the Backward Error in LSQR
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 ...
Pavel Jiránek, David Titley-Péloquin
ACL
2008
13 years 10 months ago
Semi-Supervised Convex Training for Dependency Parsing
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...
Qin Iris Wang, Dale Schuurmans, Dekang Lin
JMLR
2010
130views more  JMLR 2010»
13 years 3 months ago
A Regularization Approach to Nonlinear Variable Selection
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...
ICASSP
2011
IEEE
13 years 10 days ago
TLS-FOCUSS for sparse recovery with perturbed dictionary
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...
Xuebing Han, Hao Zhang, Huadong Meng
TNN
2008
129views more  TNN 2008»
13 years 8 months ago
Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point
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...
Johan A. K. Suykens