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T. Dittrich, P. Berger, G. Matz:
"Semi-Supervised Spectral Clustering using the Signed Laplacian";
Poster: Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA; 28.10.2018 - 31.10.2018; in: "51st Asilomar Conference on Signals, Systems, and Computers", (2018), 5 S.

Data clustering is an important step in numerous real-world problems. The goal is to separate the data into disjoint subgroups (clusters) according to some similarity metric. We consider spectral clustering (SC), where a graph captures the relation between the individual data points and the clusters are obtained from the spectrum of the associated graph Laplacian. We propose a semi-supervised SC scheme that exploits partial knowledge of the true cluster labels. These labels are used to create a modified graph with attractive intra-cluster edges (positive weights) and repulsive inter-cluster edges (negative weights). We then perform spectral clustering using the signed Laplacian matrix of the resulting signed graph. Numerical experiments illustrate the performance improvements achievable with our method.

graph signal processing, semi-supervised clustering, spectral clustering

https://publik.tuwien.ac.at/files/publik_274228.pdf