Fast optimization for mixture prior models

Authors:
Junzhou Huang;Shaoting Zhang;Dimitris Metaxas
Affiliations:
Division of Computer and Information Sciences, Rutgers University;Division of Computer and Information Sciences, Rutgers University;Division of Computer and Information Sciences, Rutgers University
Venue:
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Year:
2010

Citing 12
Cited 2

Applications of splitting algorithm to decomposition in convex programming and variational inequalities

SIAM Journal on Control and Optimization
A Modified Forward-Backward Splitting Method for Maximal Monotone Mappings

SIAM Journal on Control and Optimization
An accelerated gradient method for trace norm minimization

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence

SIAM Journal on Optimization
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM Journal on Imaging Sciences
Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems

IEEE Transactions on Image Processing
Fast image recovery using variable splitting and constrained optimization

IEEE Transactions on Image Processing
A Singular Value Thresholding Algorithm for Matrix Completion

SIAM Journal on Optimization
Fixed point and Bregman iterative methods for matrix rank minimization

Mathematical Programming: Series A and B
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

IEEE Transactions on Information Theory
Compressed sensing

IEEE Transactions on Information Theory

A review of motion analysis methods for human Nonverbal Communication Computing

Image and Vision Computing
A sparse proximal Newton splitting method for constrained image deblurring

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the minimization of a smooth convex function regularized by the mixture of prior models. This problem is generally difficult to solve even each simpler regularization problem is easy. In this paper, we present two algorithms to effectively solve it. First, the original problem is decomposed into multiple simpler subproblems. Then, these subproblems are efficiently solved by existing techniques in parallel. Finally, the result of the original problem is obtained from the weighted average of solutions of subproblems in an iterative framework. We successfully applied the proposed algorithms to compressed MR image reconstruction and low-rank tensor completion. Numerous experiments demonstrate the superior performance of the proposed algorithm in terms of both the accuracy and computational complexity.