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» On Conditions for Linearity of Optimal Estimation
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CORR
2011
Springer
167views Education» more  CORR 2011»
14 years 4 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...
PRL
2010
310views more  PRL 2010»
14 years 8 months ago
A Lagrangian Half-Quadratic approach to robust estimation and its applications to road scene analysis
We consider the problem of fitting linearly parameterized models, that arises in many computer vision problems such as road scene analysis. Data extracted from images usually cont...
Jean-Philippe Tarel, Pierre Charbonnier
ICML
2003
IEEE
15 years 10 months ago
Optimization with EM and Expectation-Conjugate-Gradient
We show a close relationship between the Expectation - Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We iden...
Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahra...
CDC
2008
IEEE
142views Control Systems» more  CDC 2008»
15 years 4 months ago
Asynchronous distributed optimization with minimal communication
— We consider problems where multiple agents must cooperate to control their individual state so as to optimize a common objective while communicating with each other to exchange...
Minyi Zhong, Christos G. Cassandras
ICCV
2007
IEEE
15 years 11 months ago
Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment
In this paper we propose a method for matching articulated shapes represented as large sets of 3D points by aligning the corresponding embedded clouds generated by locally linear ...
Diana Mateus, Fabio Cuzzolin, Radu Horaud, Edmond ...