New methods for fMRI data processing based on locally linear embeddings

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6814, Issue , 2008, Pages

  Chaillou, Laurent ^a   Yetik, Imam Samil ^a   Wernick, Miles N ^a  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Functional magnetic resonance imaging; Independent component analysis; Locally linear embeddings; Principal component analysis

Indexed keywords

(I ,J) CONDITIONS; (P ,P ,T) MEASUREMENTS; COMPUTATIONAL IMAGING; ELECTRONIC IMAGING; EMBEDDINGS; FMRI DATA; FUNCTIONAL MAGNETIC RESONANCE IMAGING (F-MRI); INDEPENDENT COMPONENT ANALYSIS (ICA); LOCAL STRUCTURES; NEW METHODS; PRINCIPAL COMPONENT ANALYSIS (PCA); TRADITIONAL METHODS;

ADMINISTRATIVE DATA PROCESSING; COMPUTATIONAL METHODS; DATA PROCESSING; EIGENVALUES AND EIGENFUNCTIONS; FINANCIAL DATA PROCESSING; FOOD PRODUCTS; IMAGING TECHNIQUES; INDEPENDENT COMPONENT ANALYSIS; MAGNETIC FIELDS; MAGNETIC RESONANCE IMAGING; MAGNETISM; MEASUREMENTS; RESONANCE; VEGETATION;

PRINCIPAL COMPONENT ANALYSIS;

EID: 44949127084     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.776453     Document Type: Conference Paper

References (15)

1. J. Schlens, "A tutorial on principal component analysis," (version 2), 2005
2. L. I. Smith, "A tutorial on PCA," 2002
3. A. Hyvärinen and E. Oja," I ndependent component analysis: algorithms and applications," 2000
4. L. De Lathauwer, B. De Moor and J. Vandewalle, "An introduction to independent component analysis," 2000
5. L. T. Sawl and S. K. Roweis, "An introduction to Locally Linear Embedding," 2001
6. L. T. Sawl and S. K. Roweis, "http://www.cs.toronto.edu/∼roweis/ lle/"
7. L. T. Sawl and S. K. Roweis, "Think globally, fit locally: unsupervised learning of low dimensional manifolds," 2003
8. B. Kim, R. Bhagalia and D. Yeo, "A realistic fMRI time series simulation with individual slice motion, geometric distortion and spin saturation effect," Proceeding of International Society of Magnetic Resonance in Medicine, 2006
9. S. Dodel, J. M. Herrmann and T. Geisel, "Components of brain activity, data analysis for fMRI," 1999
10. M. J. McKeown, S. Makeig, G. G. Brown, T. Jung, S. S. Kindermann, A. J. Bell and T. J. Sejnowski, "Analysisof fMRI data by blind separation into independent spatial components," 1998
11. C. F. Beckmann, "Probabilistic ICA for fMRI," 2004
12. X. Shen and F. G. Meyer, "Nonlinear dimension reduction and activation detection for fMRI dataset," 2006
13. D. FitzGerald, E. Coyle and B. Lawlor, "Independent subspace analysis using locally linear embedding," 2003
14. O. K. Kouropteva, O. Okun and M. Pietikäinen, "Classification of handwritten digits using supervised LLE algorithm and support vector machine"
15. F. Boschetti, "Dimensionality reduction and visualization of geoscientific images via LLE"