33 search results - page 1 / 7 » Stable Principal Component Pursuit |
CORR
2010
Springer
13 years 7 months ago
2010
Springer
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross spar...
CORR
2010
Springer
13 years 7 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...
CORR
2010
Springer
13 years 4 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...
CIKM
2010
Springer
13 years 5 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...
CORR
2012
Springer
12 years 3 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...