Spectral clustering has attracted increasing attention due to the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,
Source: A Tutorial on Spectral ClusteringPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Clustering | 422 | 44.19% |
Stochastic Block Model | 44 | 4.61% |
Community Detection | 43 | 4.50% |
Graph Clustering | 29 | 3.04% |
Semantic Segmentation | 24 | 2.51% |
graph partitioning | 19 | 1.99% |
Dimensionality Reduction | 18 | 1.88% |
Image Segmentation | 13 | 1.36% |
Computational Efficiency | 11 | 1.15% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |