Vector quantization and signal compression
Vector quantization and signal compression
Wavelets and subband coding
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Gain-shape optimized dictionary for matching pursuit video coding
Signal Processing
Learning Overcomplete Representations
Neural Computation
General design algorithm for sparse frame expansions
Signal Processing
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
Texture classification using sparse frame-based representations
EURASIP Journal on Applied Signal Processing
Efficient computational schemes for the orthogonal least squaresalgorithm
IEEE Transactions on Signal Processing
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
Subset selection in noise based on diversity measure minimization
IEEE Transactions on Signal Processing
Optimized signal expansions for sparse representation
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
Quantized overcomplete expansions in IRN: analysis, synthesis, and algorithms
IEEE Transactions on Information Theory
Sparse representations in unions of bases
IEEE Transactions on Information Theory
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Recovery of exact sparse representations in the presence of bounded noise
IEEE Transactions on Information Theory
Dictionary design for matching pursuit and application to motion-compensated video coding
IEEE Transactions on Circuits and Systems for Video Technology
Dictionary learning for sparse approximations with the majorization method
IEEE Transactions on Signal Processing
Recursive least squares dictionary learning algorithm
IEEE Transactions on Signal Processing
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
Dictionary learning for image prediction
Journal of Visual Communication and Image Representation
Sparse signal reconstruction using decomposition algorithm
Knowledge-Based Systems
Online dictionary learning algorithm with periodic updates and its application to image denoising
Expert Systems with Applications: An International Journal
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The use of overcomplete dictionaries, or frames, for sparse signal representation has been given considerable attention in recent years. The major challenges are good algorithms for sparse approximations, i.e., vector selection algorithms, and good methods for choosing or designing dictionaries/frames. This work is concerned with the latter. We present a family of iterative least squares based dictionary learning algorithms (ILS-DLA), including algorithms for design of signal dependent block based dictionaries and overlapping dictionaries, as generalizations of transforms and filter banks, respectively. In addition different constraints can be included in the ILS-DLA, thus we present different constrained design algorithms. Experiments show that ILS-DLA is capable of reconstructing (most of) the generating dictionary vectors from a sparsely generated data set, with and without noise. The dictionaries are shown to be useful in applications like signal representation and compression where experiments demonstrate that our ILS-DLA dictionaries substantially improve compression results compared to traditional signal expansions such as transforms and filter banks/wavelets.