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An informative and discriminative graph plays an important role in the graph-based semi-supervised learning methods. This paper introduces a nonnegative sparse algorithm and its approximated algorithm based on the l 0l 1 equivalence theory to compute the nonnegative sparse weights of a graph. Hence, the sparse probability graph (SPG) is termed for representing the proposed method. The nonnegative sparse weights in the graph naturally serve as clustering indicators, benefiting fordoi:10.1109/cvpr.2011.5995487 dblp:conf/cvpr/HeZHK11 fatcat:ssepzey75fbs3brbbjvjaz4w3m