14 search results - page 2 / 3 » Learning Low Rank Matrices from O(n) Entries |
ICML
2004
IEEE
14 years 6 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...
CVPR
2008
IEEE
14 years 7 months ago
2008
IEEE
From the recovery of structure from motion to the separation of style and content, many problems in computer vision have been successfully approached by using bilinear models. The...
SIAMMAX
2010
13 years 1 hour ago
2010
The problem of completing a low-rank matrix from a subset of its entries is often encountered in the analysis of incomplete data sets exhibiting an underlying factor model with app...
ICASSP
2011
IEEE
12 years 9 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...
CVPR
2007
IEEE
14 years 7 months ago
2007
IEEE
Several authors have noticed that the common representation of images as vectors is sub-optimal. The process of vectorization eliminates spatial relations between some of the near...