CORR
2010
Springer
13 years 9 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...
CORR
2010
Springer
13 years 9 months ago
2010
Springer
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span o...
ICCV
2009
IEEE
13 years 10 months ago
2009
IEEE
This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivat...
ICASSP
2009
IEEE
13 years 10 months ago
2009
IEEE
In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches ...
FAST
2009
13 years 10 months ago
2009
We present sparse indexing, a technique that uses sampling and exploits the inherent locality within backup streams to solve for large-scale backup (e.g., hundreds of terabytes) t...
WD
2010
13 years 10 months ago
2010
Gossip-based epidemic protocols are used to aggregate data in distributed systems. This fault-tolerant approach does neither require maintenance of any global network state nor kno...
ICIP
2010
IEEE
13 years 10 months ago
2010
IEEE
Semi-automated object segmentation is an important step in the cinema post-production workflow. We propose a dense motion based segmentation process that employs sparse feature ba...
BMVC
2010
13 years 10 months ago
2010
Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where c...
SIAMSC
2010
13 years 10 months ago
2010
We propose a fast algorithm for solving the ℓ1-regularized minimization problem minx∈Rn µ x 1 + Ax − b 2 2 for recovering sparse solutions to an undetermined system of linea...
PKDD
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we sho...