Abstract:Graph-based learning model has a wide range of applications in machine learning and computer vision. The key issue of the graph-based applications is to construct an informative graph to effectively represent data correlations. In practice, real-world data is usually contaminated by complex noise beyond Gaussian noise and sparse noise, which degrades learning performance dramatically. To construct a robust graph that represents real-world data distribution well, we propose a novel graph construction method. Th… Show more
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