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CORR
2011
Springer
167views Education» more  CORR 2011»
14 years 6 months ago
Fast global convergence of gradient methods for high-dimensional statistical recovery
Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
SIAMIS
2010
171views more  SIAMIS 2010»
14 years 6 months ago
Global Optimization for One-Dimensional Structure and Motion Problems
We study geometric reconstruction problems in one-dimensional retina vision. In such problems, the scene is modeled as a 2D plane, and the camera sensor produces 1D images of the s...
Olof Enqvist, Fredrik Kahl, Carl Olsson, Kalle &Ar...
TSP
2010
14 years 6 months ago
Variance-component based sparse signal reconstruction and model selection
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
Kun Qiu, Aleksandar Dogandzic
FOCM
2011
188views more  FOCM 2011»
14 years 3 months ago
Compressive Wave Computation
This paper considers large-scale simulations of wave propagation phenomena. We argue that it is possible to accurately compute a wavefield by decomposing it onto a largely incomp...
Laurent Demanet, Gabriel Peyré
ICCV
2011
IEEE
13 years 12 months ago
Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...
Guangcan Liu, Shuicheng Yan