Research map / Learning theory and algorithms / Clustering
Local Centroids Structured Non-Negative Matrix Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use multiple local centroids per cluster with sparse nearby-centroid coding and bipartite connected-component constraint, instead of single-centroid GNMF | GNMF · ORL | ACC: 0.7275 -> 0.7608 | Helped |
| use multiple local centroids per cluster with sparse nearby-centroid coding and bipartite connected-component constraint, instead of single-centroid GNMF | GNMF · UMIST | ACC: 0.6546 -> 0.6708 | Helped |
| use multiple local centroids per cluster with sparse nearby-centroid coding and bipartite connected-component constraint, instead of single-centroid GNMF | GNMF · COIL20 | ACC: 0.7530 -> 0.7783 | Helped |
| use local centroids method instead of l2,1-NMF | l2,1-NMF · PIE | ACC: 0.4191 -> 0.4583 | Helped |
| use local centroids method instead of l2,1-NMF | l2,1-NMF · PIE | NMI: 0.6810 -> 0.6428 | Hurt |
Closest papers by meaning
- Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming (ICLR 2024 · similarity 0.87)
- Fine-Grained Bipartite Concept Factorization for Clustering (CVPR 2024 · similarity 0.83)
- Unifying Proportional Fairness in Centroid and Non-Centroid Clustering (NeurIPS 2025 · similarity 0.83)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.83)
- Supervised Matrix Factorization: Local Landscape Analysis and Applications (ICML 2024 · similarity 0.83)
- Creating Coherence in Federated Non-Negative Matrix Factorization (AAAI 2025 · similarity 0.83)
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