49 search results - page 7 / 10 » Generalized low rank approximations of matrices |
SIAMJO
2010
13 years 5 months ago
2010
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood a...
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
One of the main difficulties in computing information theoretic learning (ITL) estimators is the computational complexity that grows quadratically with data. Considerable amount ...
ICPR
2010
IEEE
14 years 10 days ago
2010
IEEE
Covariance matrices have recently been a popular choice for versatile tasks like recognition and tracking due to their powerful properties as local descriptor and their low comput...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
CORR
2006
Springer
13 years 7 months ago
2006
Springer
We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classi...