Name
Abstract | ||
|---|---|---|
Data driven analysis methods such as independent component analysis (ICA) have proven to be well suited for analyzing functional magnetic resonance imaging (fMRI) data. Instead of using the independence assumption as in ICA approaches, we use the sparsity assumption to propose a novel overcomplete dictionary learning algorithm for statistical analysis of fMRI data. The proposed method differs from recent dictionary learning algorithms for sparse representation by updating all the dictionary atoms in parallel using only one SVD. Using both simulated and experimental fMRI data we show that the proposed method produces results comparable to those achieved with popular dictionary learning algorithms, but is more computationally efficient since the dictionary update is done using only one SVD. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SSP.2014.6884576 | Statistical Signal Processing |
Keywords | Field | DocType |
biomedical MRI,data analysis,image representation,independent component analysis,learning (artificial intelligence),medical image processing,singular value decomposition,ICA approach,data driven analysis methods,dictionary atoms,fMRI data analysis,functional magnetic resonance imaging data analysis,independence assumption,independent component analysis,overcomplete dictionary learning algorithm,single SVD sparse dictionary learning algorithm,sparse representation,statistical analysis,SVD,Sparse dictionary learning,functional magnetic resonance imaging (fMRI) analysis,minimum norm,sparsity assumption | Singular value decomposition,Dictionary learning,K-SVD,Pattern recognition,Computer science,Speech recognition,Artificial intelligence | Conference |
Citations | PageRank | References |
6 | 0.49 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Usman Khalid | 1 | 31 | 3.22 |
| Abd-Krim Seghouane | 2 | 78 | 12.27 |