Name
Abstract | ||
|---|---|---|
Data-driven methods, such as principal component analysis and independent component analysis, have been successfully applied to functional magnetic resonance imaging (fMRI) data in particular and neuro-imaging data in general. A central issue of these methods is the importance of correctly selecting the number of components to be used in the factor model. This issue is often addressed using a mode... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2018.2866640 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Functional magnetic resonance imaging,Eigenvalues and eigenfunctions,Covariance matrices,Data models,Data analysis,Analytical models,Principal component analysis | Data modeling,Mathematical optimization,Data-driven,Algorithm,Model selection,Curse of dimensionality,Explained sum of squares,Eigenvalues and eigenvectors,Principal component analysis,Mathematics,Asymptotic distribution | Journal |
Volume | Issue | ISSN |
38 | 2 | 0278-0062 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abd-Krim Seghouane | 1 | 193 | 24.99 |
| Navid Shokouhi | 2 | 35 | 6.43 |