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 |
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Task | Papers | Share |
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Clustering | 424 | 43.94% |
Stochastic Block Model | 45 | 4.66% |
Community Detection | 43 | 4.46% |
Graph Clustering | 29 | 3.01% |
Semantic Segmentation | 24 | 2.49% |
graph partitioning | 19 | 1.97% |
Dimensionality Reduction | 18 | 1.87% |
Image Segmentation | 13 | 1.35% |
Computational Efficiency | 11 | 1.14% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |