59 search results - page 1 / 12 » Compressive Principal Component Pursuit |
CORR
2012
Springer
12 years 4 months ago
2012
Springer
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
CORR
2010
Springer
13 years 5 months ago
2010
Springer
In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursui...
NIPS
2008
13 years 10 months ago
2008
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound...
CORR
2010
Springer
13 years 8 months ago
2010
Springer
Abstract--We consider the problem of recovering a lowrank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magn...
CIKM
2010
Springer
13 years 7 months ago
2010
Springer
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...