147 search results - page 3 / 30 » Very sparse random projections |
103
Voted
ICIP
2008
IEEE
15 years 4 months ago
2008
IEEE
Compressed sensing(CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse ba...
99
Voted
UAI
2008
14 years 11 months ago
2008
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
ICASSP
2010
IEEE
14 years 9 months ago
2010
IEEE
Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the comb...
87
Voted
ICIP
2009
IEEE
14 years 7 months ago
2009
IEEE
The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
ESA
2009
Springer
15 years 4 months ago
2009
Springer
Finding sparse cuts is an important tool for analyzing large graphs that arise in practice, such as the web graph, online social communities, and VLSI circuits. When dealing with s...