Research map / Learning theory and algorithms / Clustering
Clustering Ensemble Meets Low-rank Tensor Approximation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Refine the co-association matrix using low-rank tensor approximation and spectral clustering | Clustering ensemble · BinAlpha | ACC: 0.454 to 0.858 | Helped |
| Refine the co-association matrix using low-rank tensor approximation and spectral clustering | Clustering ensemble · MF | ACC: 0.671 to 0.990 | Helped |
| Refine the co-association matrix using low-rank tensor approximation and spectral clustering | Clustering ensemble · MNIST | ACC: 0.663 to 0.977 | Helped |
| Refine the co-association matrix using low-rank tensor approximation and spectral clustering | Clustering ensemble · Semeion | ACC: 0.739 to 0.983 | Helped |
| Use hierarchical clustering instead of the proposed method's spectral clustering | Low-rank tensor approximation clustering ensemble · CalTech20 | ACC: 0.495 to 0.726 (Ours-EA); 0.495 to 0.418 (Ours-SC) | Mixed |
| Refine the co-association matrix using low-rank tensor approximation and spectral clustering | Clustering ensemble · Texture | ACC: 0.793 to 0.993 | Helped |
| Refine the co-association matrix using low-rank tensor approximation and spectral clustering | Clustering ensemble · ISOLET | ACC: 0.581 to 0.675 | Helped |
| Use hierarchical clustering instead of spectral clustering on the refined co-association matrix | Proposed low-rank tensor approximation method · ISOLET | ACC: 0.675 to 0.575 | Hurt |
| Use a smaller lambda to penalize incorrect connections in the original co-association matrix | Proposed low-rank tensor approximation method · Seven benchmark data sets | NMI | Helped |
| Increase the number of base clusterings | Proposed low-rank tensor approximation method · Seven benchmark data sets | NMI | Helped |
Closest papers by meaning
- Generalization Performance of Ensemble Clustering: From Theory to Algorithm (ICML 2025 · similarity 0.88)
- Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model (ICLR 2024 · similarity 0.88)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.88)
- Large-scale Multi-view Tensor Clustering with Implicit Linear Kernels (CVPR 2025 · similarity 0.87)
- Enhancing Ensemble Clustering with Adaptive High-Order Topological Weights (AAAI 2024 · similarity 0.87)
- Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation (ICML 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.