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Non-Convex Total Variation Minimization for Signed Graph Cut Clustering | IEEE Conference Publication | IEEE Xplore

Non-Convex Total Variation Minimization for Signed Graph Cut Clustering


Abstract:

We consider graph cut minimization in signed graphs with three clusters. To this end, we use the signed total variation, which is convex and has shown promising results i...Show More

Abstract:

We consider graph cut minimization in signed graphs with three clusters. To this end, we use the signed total variation, which is convex and has shown promising results in our previous work on semi-supervised clustering. Here, we consider the unsupervised case and use a non-convex norm-constraint to avoid degenerate solutions. We solve the resulting minimization problem via the non-convex ADMM and derive conditions on the graph topology that guarantee convergence under rather weak conditions on the graph. In our numerical experiments we study the labeled stochastic block model and compare our method to a state-of-the-art clustering algorithm. The results are convincing both with regard to the fraction of mislabeled nodes and the signed cut.
Date of Conference: 31 October 2021 - 03 November 2021
Date Added to IEEE Xplore: 04 March 2022
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Conference Location: Pacific Grove, CA, USA

References

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