Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Dimensionality Reduction for Sparse Representation Based Face Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Robust classification using structured sparse representation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning the sparse representation for classification
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Regularization parameter estimation for feedforward neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Theory
Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections
IEEE Transactions on Image Processing
Fisher Discrimination Dictionary Learning for sparse representation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Improving fusion with optimal weight selection in Face Recognition
Integrated Computer-Aided Engineering
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Sparse representation SR or sparse coding SC, which assumes the data vector can be sparse represented by linear combination over basis vectors, has been successfully applied in machine learning and computer vision tasks. In order to solve sparse representation problem, regularization technique is applied to constrain the sparsity of coefficients of linear representation. In this paper, a reconstruction-error-based adaptive regularization parameter estimation method is proposed to improve the representation ability of SR. The adaptive regularization parameter aims to balance the reconstruction error and the sparsity of coefficient vector and to minimize reconstruction error. Substantial experiments are performed on some benchmark databases. Simulation results demonstrate that this adaptive regularization parameter estimation method can find a proper parameter for each test sample, consequently, can improve the accuracy of SR and eliminate a time-consuming cross-validation process.