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» Better Size Estimation for Sparse Matrix Products
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CORR
2008
Springer
92views Education» more  CORR 2008»
13 years 5 months ago
Convex Sparse Matrix Factorizations
We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the usual explicit upper bound on the dictionary size by...
Francis Bach, Julien Mairal, Jean Ponce
HPCN
1997
Springer
13 years 9 months ago
Parallel Solution of Irregular, Sparse Matrix Problems Using High Performance Fortran
For regular, sparse, linear systems, like those derived from regular grids, using High Performance Fortran (HPF) for iterative solvers is straightforward. However, for irregular ma...
Eric de Sturler, Damian Loher
CCECE
2006
IEEE
13 years 9 months ago
Variable Step-Size Affine Projection Algorithm with a Weighted and Regularized Projection Matrix
This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projec...
Tao Dai, Andy Adler, Behnam Shahrrava
PR
2008
144views more  PR 2008»
13 years 5 months ago
Kernel quadratic discriminant analysis for small sample size problem
It is generally believed that quadratic discriminant analysis (QDA) can better fit the data in practical pattern recognition applications compared to linear discriminant analysis ...
Jie Wang, Konstantinos N. Plataniotis, Juwei Lu, A...
ICDM
2010
IEEE
146views Data Mining» more  ICDM 2010»
13 years 3 months ago
One-Class Matrix Completion with Low-Density Factorizations
Consider a typical recommendation problem. A company has historical records of products sold to a large customer base. These records may be compactly represented as a sparse custom...
Vikas Sindhwani, Serhat Selcuk Bucak, Jianying Hu,...