49 search results - page 6 / 10 » Generalized low rank approximations of matrices |
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
—Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization problems. In this paper, we define weak, sectional and strong recovery for NNM to...
TKDE
2012
11 years 10 months ago
2012
—Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorith...
SCL
2008
13 years 7 months ago
2008
ABSTRACT: Linear function approximations based on random projections are proposed and justified for a class of fixed point and minimization problems. KEY WORDS: random projections,...
CVPR
2009
IEEE
13 years 11 months ago
2009
IEEE
Nonnegative Matrix Factorization (NMF) approximates a given data matrix as a product of two low rank nonnegative matrices, usually by minimizing the L2 or the KL distance between ...
COMPUTING
2006
13 years 7 months ago
2006
The ULV decomposition (ULVD) is an important member of a class of rank-revealing two-sided orthogonal decompositions used to approximate the singular value decomposition (SVD). Th...