ICIP
2009
IEEE
13 years 10 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...
SCL
2008
14 years 11 days ago
2008
ABSTRACT: Linear function approximations based on random projections are proposed and justified for a class of fixed point and minimization problems. KEY WORDS: random projections,...
BIOINFORMATICS
2006
14 years 15 days ago
2006
Summary: We present a new R package for the assessment of the reliability of clusters discovered in high dimensional DNA microarray data. The package implements methods based on r...
CORR
2010
Springer
14 years 16 days ago
2010
Springer
This paper presents a new analysis for the orthogonal matching pursuit (OMP) algorithm. It is shown that if the restricted isometry property (RIP) is satisfied at sparsity level O...
SDM
2007
SIAM
14 years 1 months ago
2007
SIAM
In recent years, random projection has been used as a valuable tool for performing dimensionality reduction of high dimensional data. Starting with the seminal work of Johnson and...
SDM
2007
SIAM
14 years 1 months ago
2007
SIAM
We propose the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch al...
NIPS
2007
14 years 1 months ago
2007
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
APPROX
2008
Springer
14 years 2 months ago
2008
Springer
Random projection methods give distributions over k
COLT
2006
Springer
14 years 4 months ago
2006
Springer
Abstract. We present an improved version of random projections that takes advantage of marginal norms. Using a maximum likelihood estimator (MLE), marginconstrained random projecti...
INCDM
2007
Springer
14 years 6 months ago
2007
Springer
Abstract. Clustering algorithms for multidimensional numerical data must overcome special difficulties due to the irregularities of data distribution. We present a clustering algo...