Saarland University, Machine Learning Group, Fak. MI - Mathematik und Informatik, Campus E1 1, 66123 Saarbrücken, Germany

Machine Learning Group
Department of Mathematics and Computer Science - Saarland University

# 1-SPECTRAL CLUSTERING

Spectral clustering is a graph-based clustering method based on a relaxation of the NP-hard problem of finding the optimal balanced cut of an undirected weighted graph where the weights represent the similiarities between points.

The balanced cut criteria to be optimized are the Ratio Cheeger cut (RCC), defined for a partition (C,C) where C=V \ C as

or the Normalized Cheeger cut (NCC), where the cardinalities of the clusters are replaced by the sums of degrees.

The standard spectral relaxation has as its solution the second eigenvector of the graph Laplacian and the final partition is found by optimal thresholding.

In [2] we proposed p-spectral clustering, a generalization of spectral clustering based on the second eigenvector of the nonlinear graph p-Laplacian (the graph Laplacian is recovered for p=2). One can show that for p approaching 1 the cut found by thresholding the second eigenvector converges to the optimal Cheeger cut, and indeed one obtains much better cuts, at the expense of higher runtime. In [1] we considered directly the case p=1 and showed that the second eigenvalue of the graph 1-Laplacian is equal to the optimal Cheeger cut.

Our nonlinear inverse power method can be applied to efficiently compute nonconstant nonlinear eigenvectors of the graph 1-Lapacian and the resulting 1-spectral clustering. An implementation can be downloaded from this website.

1-Spectral Clustering has been developed by Matthias Hein and Thomas Bühler, Department of Computer Science, Saarland University, Germany. The code for 1-Spectral Clustering is published as free software under the terms of the GNU GPL v3.0. Please include a reference to the paper An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA and include the original documentation and copyright notice.

## REFERENCES

[2] T. Bühler, M. Hein,
Spectral Clustering based on the graph p-Laplacian,
In Leon Bottou and Michael Littman, editors, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), 81-88, Omnipress, 2009, PDF  (Supplementary material: PDF  - Errata of Supp. Mat.: PDF )

## DOCUMENTATION

To run 1-Spectral Clustering, download the file OneSpectralClustering.rar and unrar it in some convenient directory. Add this directory to the Matlab path variable. The clustering can then be computed by the function 'OneSpectralClustering'. If you find bugs or have other comments contact Thomas Bühler or Matthias Hein.

Usage, fast version:

[clusters,cuts,cheegers] = OneSpectralClustering(W,criterion,k);

Usage, slower version, but improved partitioning (recommended):

[clusters,cuts,cheegers] = OneSpectralClustering(W,criterion,k,numOuter,numInner);

[clusters,cuts,cheegers] = OneSpectralClustering(W,criterion,k,numOuter,numInner,verbosity);

Input:

W: Sparse weight matrix. Has to be symmetric.

criterion: The multipartition criterion to be optimized. Available choices are

'ncut' - Normalized Cut,
'ncc' - Normalized Cheeger Cut,
'rcut' - Ratio Cut,
'rcc' - Ratio Cheeger Cut

k: number of clusters

numOuter: number of additional times the multipartitioning scheme is performed (optional, default is 0);

numInner: for the additional runs of the multipartitioning scheme: number of random initializations at each level (optional, default is 0).

verbosity: Controls how much information is displayed. Levels 0-3 (optional, default is 2).

If the parameters numOuter and numInner are not specified, the multipartitioning scheme is performed once, where each subpartitioning problem is initialized with the second eigenvector of the standard graph Laplacian (fast version). The quality of the obtained partitioning can be improved by performing additional runs of the multipartitioning scheme (parameter numOuter) with multiple random initializations at each level (parameter numInner).

Output:

clusters: mx(k-1) matrix containing in each column the computed clustering for each partitioning step.

cuts: (k-1)x1 vector containing the Ratio/Normalized Cut values after each partitioning step.

cheegers: (k-1)x1 vector containing the Ratio/Normalized Cheeger Cut values after each partitioning step.

The final clustering is obtained via clusters(:,end), the corresponding cut/cheeger values via cuts(end), cheegers(end). If more flexibility is desired, call the subroutine computeMultiPartitioning directly.