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CORR
2010
Springer
134views Education» more  CORR 2010»
13 years 6 months ago
The LASSO risk for gaussian matrices
We consider the problem of learning a coefficient vector x0 ∈ RN from noisy linear observation y = Ax0 + w ∈ Rn . In many contexts (ranging from model selection to image proce...
Mohsen Bayati, Andrea Montanari
CVPR
2011
IEEE
13 years 3 months ago
Local Isomorphism to Solve the Pre-image Problem in Kernel Methods
Kernel methods have been popular over the last decade to solve many computer vision, statistics and machine learning problems. An important, both theoretically and practically, op...
Dong Huang, Yuandong Tian, Fernando DelaTorre
TSP
2010
13 years 2 months ago
Double sparsity: learning sparse dictionaries for sparse signal approximation
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
Ron Rubinstein, Michael Zibulevsky, Michael Elad
SIAMSC
2010
141views more  SIAMSC 2010»
13 years 6 months ago
An Iterative Method for Edge-Preserving MAP Estimation When Data-Noise Is Poisson
In numerous applications of image processing, e.g. astronomical and medical imaging, data-noise is well-modeled by a Poisson distribution. This motivates the use of the negative-lo...
Johnathan M. Bardsley, John Goldes
PAMI
2008
198views more  PAMI 2008»
13 years 7 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation....
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...