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Decomposable triangulated graphs have been shown to be efficient and effective for modeling the probabilistic spatio-temporal structure of brief stretches of human motion. In previous work such model structure was handcrafted by expert human observers and labeled data were needed for parameter learning. We present a method to build automatically the structure of the decomposable triangulated graph from unlabeled data. It is based on maximum-likelihood. Taking the labeling of the data as hiddendoi:10.1109/cvpr.2001.991043 dblp:conf/cvpr/SongGP01 fatcat:sttkk4xjgnegdcfae5i2u4yrli