Research map / Learning theory and algorithms / Clustering
Word Co-Occurrence Regularized Non-Negative Matrix Tri-Factorization for Text Data Co-Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add word co-occurrence (SPPMI) regularization to NMTF (ablation) | NMTF · CSTR | NMI: 0.67 -> 0.76 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · CLASSIC4 | NMI: 0.55 -> 0.72 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · NG20 | NMI: 0.40 -> 0.63 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · TREC | NMI: 0.59 -> 0.67 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · LA Times | NMI: 0.42 -> 0.53 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · NG20 | ARI: 0.23 -> 0.47 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · CLASSIC4 | ARI: 0.44 -> 0.71 | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · word clusters on 4 datasets | average PMI | Helped |
| add word co-occurrence regularization to NMTF (ablation) | NMTF · word representations | cosine similarity of same-class top words | Helped |
| vary regularization lambda (ablation) | WC-NMTF · five datasets | NMI/ARI | No clear effect |
| increase latent dimension m from number of clusters g up to 300 (ablation) | WC-NMTF · five datasets | NMI/ARI | Hurt |
Closest papers by meaning
- CoDiNMF: Co-Clustering of Directed Graphs via NMF (AAAI 2018 · similarity 0.87)
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- Fine-Grained Bipartite Concept Factorization for Clustering (CVPR 2024 · similarity 0.83)
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- New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering (ICML 2024 · similarity 0.82)
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