Clustered Component Analysis for FMRI Signal Estimation and Classification

In this paper, we introduce a method for estimating the statistically distinct neural responses in an sequence of functional magnetic resonance images (fMRI). The crux of our method is a technique which we call clustered component analysis (CCA). Clustered component analysis is a method for identifying the distinct component vectors in a multivariate data set. CCA is distinct from principal components analysis (PCA), and independent components analysis (ICA), because it is not constrained to produce orthogonal component vectors and it does not assume that components are indepedent. CCA employs Bayesian estimation methods such as expectationmaximization (EM) and Rissanen order identification to determine the best set of component vectors.

• Type: Conference
• Year: 2000
• Where: ICIP
• Authors: Charles A. Bouman, Sea Chen, Mark J. Lowe
• Keywords: Component Analysis, Distinct Component Vectors, ICIP 2000, Image Processing, Orthogonal Component Vectors