The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Adaptive system identification and signal processing algorithms
Adaptive system identification and signal processing algorithms
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
What is the goal of sensory coding?
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Minimax entropy principle and its application to texture modeling
Neural Computation
Neural Computation
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
A Variational Method for Learning Sparse and Overcomplete Representations
Neural Computation
Learning Overcomplete Representations
Neural Computation
Affine scaling transformation based methods for computing low complexity sparse solutions
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
Sparse basis selection, ICA, and majorization: towards a unified perspective
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
An affine scaling methodology for best basis selection
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Analysis of sparse representation and blind source separation
Neural Computation
Sparse representations of polyphonic music
Signal Processing - Sparse approximations in signal and image processing
Learning Sparse Overcomplete Codes for Images
Journal of VLSI Signal Processing Systems
Learning Sparse Overcomplete Codes for Images
Journal of VLSI Signal Processing Systems
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
Neural Computation
Sparse coding via thresholding and local competition in neural circuits
Neural Computation
Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing
Neural Information Processing
BICA: A Boolean Indepenedent Component Analysis Approach
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
CG-M-FOCUSS and Its Application to Distributed Compressed Sensing
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Continuous speech recognition with sparse coding
Computer Speech and Language
Manifold models for signals and images
Computer Vision and Image Understanding
K-hyperline clustering learning for sparse component analysis
Signal Processing
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
Journal of Mathematical Imaging and Vision
Improved FOCUSS method with conjugate gradient iterations
IEEE Transactions on Signal Processing
Dictionary learning for sparse approximations with the majorization method
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
Parametric dictionary design for sparse coding
IEEE Transactions on Signal Processing
Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Dictionary learning for L1-exact sparse coding
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Learning sparse representation using iterative subspace identification
IEEE Transactions on Signal Processing
Dictionary identification: sparse matrix-factorization via l1-minimization
IEEE Transactions on Information Theory
Two conditions for equivalence of 0-norm solution and 1-norm solution in sparse representation
IEEE Transactions on Neural Networks
Recursive least squares dictionary learning algorithm
IEEE Transactions on Signal Processing
Blind compressed sensing: theory
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Learning the Morphological Diversity
SIAM Journal on Imaging Sciences
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
A novel predual dictionary learning algorithm
Journal of Visual Communication and Image Representation
The Sample Complexity of Dictionary Learning
The Journal of Machine Learning Research
Efficient minimization for dictionary based sparse representation and signal recovery
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Archetypal analysis for machine learning and data mining
Neurocomputing
Feature fusion for 3D hand gesture recognition by learning a shared hidden space
Pattern Recognition Letters
K-EVD clustering and its applications to sparse component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Recovery of sparse representations by polytope faces pursuit
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Sparse representations and sphere decoding for array signal processing
Digital Signal Processing
Learning multi-modal dictionaries: application to audiovisual data
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
K-clustered tensor approximation: A sparse multilinear model for real-time rendering
ACM Transactions on Graphics (TOG)
From high definition image to low space optimization
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
An augmented Lagrangian approach to general dictionary learning for image denoising
Journal of Visual Communication and Image Representation
BTF compression via sparse tensor decomposition
EGSR'09 Proceedings of the Twentieth Eurographics conference on Rendering
Nonconvex sparse regularizer based speckle noise removal
Pattern Recognition
Neural associative memories and sparse coding
Neural Networks
Accelerating non-local denoising with a patch based dictionary
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Object recognition using sparse representation of overcomplete dictionary
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Dictionary learning based sparse coefficients for audio classification with max and average pooling
Digital Signal Processing
i-Vector with sparse representation classification for speaker verification
Speech Communication
Learning Big (Image) Data via Coresets for Dictionaries
Journal of Mathematical Imaging and Vision
Online dictionary learning algorithm with periodic updates and its application to image denoising
Expert Systems with Applications: An International Journal
Ensemble dictionary learning for saliency detection
Image and Vision Computing
Hi-index | 0.09 |
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial "25 words or less"), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations.Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).