Research map / Learning theory and algorithms / Clustering
Centerless Multi-View K-means Based on the Adjacency Matrix
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adjacency-derived distances instead of Euclidean distances | Centerless Multi-View K-Means · Three-ring toy dataset | clustering performance | Helped |
| Use the proposed method instead of LTCPS | Multi-view clustering · MSRC | ACC: 0.981 -> 1.000 | Helped |
| Use the proposed method instead of ETLMSC | Multi-view clustering · HW | ACC: 0.938 -> 0.998 | Helped |
| Use the proposed method instead of LTCPS | Multi-view clustering · ORL | ACC: 0.981 -> 1.000 | Helped |
| Use the proposed method instead of ETLMSC | Multi-view clustering · Mnist4 | ACC: 0.934 -> 0.988 | Helped |
| Use the proposed method instead of Co-Reg | Multi-view clustering · Reuters | ACC: 0.563 -> 0.682 | Helped |
| Use the proposed method instead of SC (best) | Multi-view clustering · NUS-WIDE | ACC: 0.131 -> 0.510 | Helped |
| Vary parameter r from 3 to 9 (ablation) | Centerless Multi-View K-Means · MSRC and ORL | ACC, NMI, and Purity | No clear effect |
| Set parameter r to 10 (ablation) | Centerless Multi-View K-Means · MSRC | clustering performance: 0.272 | Hurt |
| Vary tensor Schatten parameter p from 0.1 to 1.0 (ablation) | Centerless Multi-View K-Means · MSRC and ORL | ACC, NMI, and Purity: 0.022 | No clear effect |
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