Semi-Supervised Cluster Extraction via a Compressive Sensing Approach
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We use techniques from compressive sensing to design a local clustering algorithm by treating the cluster indicator vector as a sparse solution to a linear system whose coefficient matrix is the graph Laplacian. If the graph is drawn from the Stochastic Block Model we are able to prove that the fraction of misclassified vertices goes to zero as the size of the graph increases. Numerical experiments on simulated and real-life graphs demonstrate the effectiveness and speed of our approach. Finally, we explore the application of our algorithm to semi-supervised learning.
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