Classification Via Compressed Random Fields

A classification method based on random field models is derived. A few innovative concepts that are incorporated into the algorithm such as efficient training via Kalman filtering and adaptation via extended Baum-Welch (EBW) demonstrate improvement both in computational complexity and classification accuracy. The most significant contribution of this work, however, is the derivation of an online compression and training mechanism that is capable of representing the sparsity patterns which arise naturally in the high dimensional data sets. Thus, the improved classifier uses compressed sensing techniques for learning the statistical relations within the random field models. The resulting representations are compressed in the sense that only few connections are considered for each node. The performance of the new algorithm is demonstrated in fMRI classification.

By: Avishy Carmi; Guillermo Cecchi; Dimitri Kanevsky; Bhuvana Ramabhadran; Irina Rish

Published in: RC24740 in 2009


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