CORR
2010
Springer
13 years 4 months ago
2010
Springer
This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal mat...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
In this paper we revisit the sparse multiple measurement vector (MMV) problem, where the aim is to recover a set of jointly sparse multichannel vectors from incomplete measurement...
ICA
2010
Springer
13 years 4 months ago
2010
Springer
SMALLbox is a new foundational framework for processing signals, using adaptive sparse structured representations. The main aim of SMALLbox is to become a test ground for explorati...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
Given a set of data points drawn from multiple low-dimensional linear subspaces of a high-dimensional space, we consider the problem of clustering these points according to the su...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
We consider the problem of estimating a deterministic sparse vector x0 from underdetermined measurements Ax0 + w, where w represents white Gaussian noise and A is a given determin...
CIMAGING
2009
13 years 5 months ago
2009
The surface reflectance function of many common materials varies slowly over the visible wavelength range. For this reason, linear models with a small number of bases (5-8) are fr...
BMVC
2000
13 years 5 months ago
2000
This paper addresses the problem of reconstructing surface models of indoor scenes from sparse 3D scene structure captured from N camera views. Sparse 3D measurements of real scen...
NIPS
2003
13 years 5 months ago
2003
In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K−means method. We have proved ...
NIPS
2007
13 years 6 months ago
2007
Automatic relevance determination (ARD) and the closely-related sparse Bayesian learning (SBL) framework are effective tools for pruning large numbers of irrelevant features leadi...