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TIT
2008
121views more  TIT 2008»
13 years 11 months ago
Stability Results for Random Sampling of Sparse Trigonometric Polynomials
Recently, it has been observed that a sparse trigonometric polynomial, i.e. having only a small number of non-zero coefficients, can be reconstructed exactly from a small number o...
Holger Rauhut
FOCM
2008
156views more  FOCM 2008»
13 years 11 months ago
Random Sampling of Sparse Trigonometric Polynomials, II. Orthogonal Matching Pursuit versus Basis Pursuit
We investigate the problem of reconstructing sparse multivariate trigonometric polynomials from few randomly taken samples by Basis Pursuit and greedy algorithms such as Orthogona...
Stefan Kunis, Holger Rauhut
IJON
2007
184views more  IJON 2007»
13 years 11 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
ICASSP
2010
IEEE
13 years 11 months ago
Robust regression using sparse learning for high dimensional parameter estimation problems
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...
Kaushik Mitra, Ashok Veeraraghavan, Rama Chellappa
STOC
1993
ACM
123views Algorithms» more  STOC 1993»
14 years 3 months ago
Constructing small sample spaces satisfying given constraints
Abstract. The subject of this paper is nding small sample spaces for joint distributions of n discrete random variables. Such distributions are often only required to obey a certa...
Daphne Koller, Nimrod Megiddo