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For the graph-based semi-supervised learning, the performance of a classifier is very sensitive to the structure of the graph. So constructing a good graph to represent data, a proper structure for the graph is quite critical. This paper proposes a novel model to construct the graph structure for semi-supervised learning. In this new structure, the weights of edges in the graph are obtained by the linear combination of a Nonnegative Sparse graph and K Nearest Neighbour graph (NSKNN-graph). The N
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