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» Exploiting Statistical Dependencies in Sparse Representation...
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ICASSP
2011
IEEE
12 years 9 months ago
Sparse decomposition of transformation-invariant signals with continuous basis pursuit
Consider the decomposition of a signal into features that undergo transformations drawn from a continuous family. Current methods discretely sample the transformations and apply s...
Chaitanya Ekanadham, Daniel Tranchina, Eero P. Sim...
TIT
2010
174views Education» more  TIT 2010»
12 years 12 months ago
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation
Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few ...
Jarvis Haupt, Waheed Uz Zaman Bajwa, Gil M. Raz, R...
SIGPRO
2011
209views Hardware» more  SIGPRO 2011»
13 years 6 days ago
Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms
In this paper, we survey and compare different algorithms that, given an overcomplete dictionary of elementary functions, solve the problem of simultaneous sparse signal approxim...
A. Rakotomamonjy
CVPR
2010
IEEE
13 years 9 months ago
Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification
Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization pro...
Shenghua Gao, Wai-Hung Tsang, Liang-Tien Chia, Pei...
ICASSP
2008
IEEE
13 years 11 months ago
Time-frequency localization from sparsity constraints
In the case of multicomponent AM-FM signals, the idealized representation which consists of weighted trajectories on the time-frequency (TF) plane, is intrinsically sparse. Recent...
Pierre Borgnat, Patrick Flandrin