Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Atomic Decomposition by Basis Pursuit
SIAM Review
Dictionary learning algorithms for sparse representation
Neural Computation
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
An affine scaling methodology for best basis selection
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Recursive least squares dictionary learning algorithm
IEEE Transactions on Signal Processing
| Hi-index | 0.09 |
Sparse representation (SR) for signals over an overcomplete dictionary fascinates a lot of researchers in the past decade. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. This paper addresses the problem of dictionary generation in SR. Recent studies show that this problem is equivalent to the problem of codebook estimation in vector quantization (VQ). A kernel fuzzy codebook estimation (KFCE) algorithm is proposed in this paper. The principle of the KFCE algorithm is to integrate the distance kernel trick with the fuzzy clustering algorithm to generate dictionary for SR. Experimental results on real image data show that the KFCE is fit for generating dictionary for SR.