Name
Abstract | ||
|---|---|---|
The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection. However, in real-life applications the number of clusters or communities (say, $K$) is generally unknown a-priori. Consequently, the majority of the existing methods either choose $K$ heuristically or they repeat the clustering method with different choices of $K$ and accept the best clustering result. The first option, more often, yields suboptimal result, while the second option is computationally expensive. In this work, we propose an incremental method for constructing the eigenspectrum of the graph Laplacian matrix. This method leverages the eigenstructure of graph Laplacian matrix to obtain the $K$-th eigenpairs of the Laplacian matrix given a collection of all the $K-1$ smallest eigenpairs. Our proposed method adapts the Laplacian matrix such that the batch eigenvalue decomposition problem transforms into an efficient sequential leading eigenpair computation problem. As a practical application, we consider user-guided spectral clustering. Specifically, we demonstrate that users can utilize the proposed incremental method for effective eigenpair computation and determining the desired number of clusters based on multiple clustering metrics. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Laplacian matrix,Spectral clustering,Mathematical optimization,Heuristic,Correlation clustering,Matrix (mathematics),Computer science,Eigendecomposition of a matrix,Artificial intelligence,Cluster analysis,Eigenvalues and eigenvectors,Machine learning |
DocType | Volume | Citations |
Journal | abs/1512.07349 | 8 |
PageRank | References | Authors |
0.43 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pin-Yu Chen | 1 | 646 | 74.59 |
| Baichuan Zhang | 2 | 108 | 7.30 |
| Mohammad Al Hasan | 3 | 427 | 35.08 |
| Alfred O. Hero III | 4 | 2600 | 301.12 |