Classification in High-Dimensional Spaces Using Markov Random Field Models with Application to fMRI Analysis

We present a new classification algorithm for high dimensional problems. The algorithm uses a Markov random field for modeling meaningful interactions within the training data set. The model parameters are efficiently estimated using the Kalman filter algorithm and adapted to fit the test data using a recursive matrix formulation of the extended Baum-Welch algorithm. A spatially likelihood test procedure is then used for classifying the data. The performance of the new algorithm is demonstrated in fMRI classification.

By: Avishy Carmi; Dimitri Kanevsky; Bhuvana Ramabhadran

Published in: RC24648 in 2008


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