49 search results - page 1 / 10 » Generalized low rank approximations of matrices |
86
Voted
PRL
2006
14 years 10 months ago
2006
: As an extension to 2DPCA, Generalized Low Rank Approximation of Matrices (GLRAM) applies two-sided (i.e., the left and right) rather than single-sided (i.e., the left or the righ...
107
Voted
ICML
2004
IEEE
15 years 11 months ago
2004
IEEE
The problem of computing low rank approximations of matrices is considered. The novel aspect of our approach is that the low rank approximations are on a collection of matrices. W...
101
Voted
TNN
2010
14 years 5 months ago
2010
Compared to Singular Value Decomposition (SVD), Generalized Low Rank Approximations of Matrices (GLRAM) can consume less computation time, obtain higher compression ratio, and yiel...
98
Voted
CORR
2011
Springer
14 years 2 months ago
2011
Springer
We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient...
87
Voted
SDM
2007
SIAM
15 years 10 days ago
2007
SIAM
This paper presents a unified view of a number of dimension reduction techniques under the common framework of tensors. Specifically, it is established that PCA, and the recentl...