The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Iterated Hard Shrinkage for Minimization Problems with Sparsity Constraints
SIAM Journal on Scientific Computing
Sparse principal component analysis via regularized low rank matrix approximation
Journal of Multivariate Analysis
Expectation-maximization for sparse and non-negative PCA
Proceedings of the 25th international conference on Machine learning
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Generalized Power Method for Sparse Principal Component Analysis
The Journal of Machine Learning Research
Improve robustness of sparse PCA by L1-norm maximization
Pattern Recognition
A closed form solution to robust subspace estimation and clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
IEEE Transactions on Image Processing
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A new method is proposed in this paper to learn overcomplete dictionary from signals. Differing from the current methods that enforce uniform sparsity constraint on the coefficients of each input signal, the proposed method attempts to impose global sparsity constraint on the coefficient matrix of the entire signal set. This enables the proposed method to fittingly assign the atoms of the dictionary to represent various signals and optimally adapt to the complicated structures underlying the entire signal set. By virtue of the sparse coding and sparse PCA techniques, a simple algorithm is designed for the implementation of the method. The efficiency and the convergence of the proposed algorithm are also theoretically analyzed. Based on the experimental results implemented on a series of signal and image data sets, the capability of the proposed method is substantiated in original dictionary recovering, signal reconstructing and salient signal structure revealing.