Spectral clustering based on the graph p-Laplacian

By Thomas Bühler, Matthias Hein. International Conference on Machine Learning, 2009

346 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Must-Link Constraints for Video Segmentation Based on Spectral Clustering (2014, methodology): “Here we tailor the general theory to a video segmentation framework based on the normalized cut, solved either via the spectral [39, 34] or 1-spectral [9, 21] relaxation.”
Constrained 1-Spectral Clustering (2012, methodology): “Additional experimental results are given in Tables 5 and 6 for the datasets given in Table 6.1”
Piecewise Flat Embedding for Image Segmentation (2018, methodology): “B¨uhler and Hein [12] utilize a uniform p -Laplacian formulation to deﬁne spectral embedding methods with various norms and prove that the optimal solution of Cheeger Cut can be found by thresholding the second eigenvector of the p -Laplacian when p → 1 .”