Abstract In graph-based machine learning algorithms, the construction of the graph representing the data structure is the key issue. In this paper, a non-negative and sparse graph construction algorithm based on split Bregman method is presented. A weight matrix is learned by solving an equality formulation of the sparse representation through split Bregman method. In the weight matrix, each data sample can be represented by a non-negative linear combination of other samples. The constructed graph of the proposed algorithm can capture the linear relationship between data samples. Experimental results under semi-supervised learning framework demonstrate that the proposed algorithm can capture the latent structure information of data well.
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2015, Vol. 28 
