Topics in matrix analysis
Atomic Decomposition by Basis Pursuit
SIAM Review
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Optimizing the performance of sparse matrix-vector multiplication
Optimizing the performance of sparse matrix-vector multiplication
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Method of optimal directions for frame design
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Compression of facial images using the K-SVD algorithm
Journal of Visual Communication and Image Representation
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Image sequence denoising via sparse and redundant representations
IEEE Transactions on Image Processing
Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary
SIAM Journal on Imaging Sciences
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Dictionary Preconditioning for Greedy Algorithms
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Why Simple Shrinkage Is Still Relevant for Redundant Representations?
IEEE Transactions on Information Theory
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Blind compressed sensing: theory
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
SMALLbox - an evaluation framework for sparse representations and dictionary learning algorithms
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Learning adaptive and sparse representations of medical images
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Regularized latent semantic indexing
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Contextual dictionaries for image super resolution
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Image decomposition via learning the morphological diversity
Pattern Recognition Letters
Bioinspired sparse spectro-temporal representation of speech for robust classification
Computer Speech and Language
Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling
ACM Transactions on Information Systems (TOIS)
Image primitive coding and visual quality assessment
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Dictionary learning for image prediction
Journal of Visual Communication and Image Representation
Design of non-linear discriminative dictionaries for image classification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
A fast mixing matrix estimation method in the wavelet domain
Signal Processing
Engineering Applications of Artificial Intelligence
| Hi-index | 35.68 |
An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = ΦA, where Φ is a fixed base dictionary and A is sparse. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the gap between implicit dictionaries, which have efficient implementations yet lack adaptability, and explicit dictionaries, which are fully adaptable but non-efficient and costly to deploy. In this paper, we discuss the advantages of sparse dictionaries, and present an efficient algorithm for training them. We demonstrate the advantages of the proposed structure for 3-D image denoising.