CORR
2010
Springer
13 years 1 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 1 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 2 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 2 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 2 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 2 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 2 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 2 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 2 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 2 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...