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Markov Random Fields for Classification in High Dimensional Spaces with Application to fMRI Analysis

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In this talk 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.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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BAS Seminar Room 2; https://ukri.zoom.us/j/93688883186 Technology DAMTP

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