Name
Abstract | ||
|---|---|---|
A principal component analysis (PCA) based dictionary initialization approach accompanied by a computationally efficient dictionary learning algorithm for statistical analysis of functional magnetic resonance imaging (fMRI) is proposed. It replaces a singular value decomposition (SVD) computation with an approximate solution to obtain a local minima for a given initial dictionary. The K-SVD has been recently used to develop a data-driven sparse general linear model (GLM) framework for fMRI analysis solely based on the sparsity of signals. However, the K-SVD algorithm is computationally demanding and may require many iterations to converge. Replacing SVD with an approximate solution for the dictionary update combined with an optimal dictionary initialization, the desired results for a sparse GLM can be improved and achieved in few iterations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6867805 | Biomedical Imaging |
Keywords | Field | DocType |
biomedical MRI,maximum likelihood estimation,medical image processing,principal component analysis,singular value decomposition,K-SVD algorithm,PCA,computationally efficient dictionary learning algorithm,data-driven sparse general linear model framework,fMRI,functional magnetic resonance imaging,iterations,optimal dictionary initialization,principal component analysis,signal sparsity,singular value decomposition computation,sparse GLM framework,statistical analysis,EDL,EK-SVD,K-SVD,MOD,fMRI | Sparse PCA,Dictionary learning,Pattern recognition,K-SVD,Computer science,Maximum likelihood,Speech recognition,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1945-7928 | 5 | 0.46 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Usman Khalid | 1 | 31 | 3.22 |
| Abd-Krim Seghouane | 2 | 78 | 12.27 |