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ICANN
2007
Springer
15 years 4 months ago
Sparse Least Squares Support Vector Regressors Trained in the Reduced Empirical Feature Space
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...
Shigeo Abe, Kenta Onishi
ICASSP
2010
IEEE
14 years 10 months ago
Sparsity-cognizant overlapping co-clustering for behavior inference in social networks
Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping submatrix factors (co-clusters). This combinat...
Hao Zhu, Gonzalo Mateos, Georgios B. Giannakis, Ni...
SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
15 years 4 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
SIAMSC
2008
131views more  SIAMSC 2008»
14 years 9 months ago
Gramian-Based Model Reduction for Data-Sparse Systems
Model order reduction (MOR) is common in simulation, control and optimization of complex dynamical systems arising in modeling of physical processes and in the spatial discretizati...
Ulrike Baur, Peter Benner
ICCAD
2009
IEEE
133views Hardware» more  ICCAD 2009»
14 years 7 months ago
A parallel preconditioning strategy for efficient transistor-level circuit simulation
A parallel computing approach for large-scale SPICE-accurate circuit simulation is described that is based on a new preconditioned iterative solver. The preconditioner involves the...
Heidi Thornquist, Eric R. Keiter, Robert J. Hoekst...