Research map / Learning theory and algorithms / Clustering
Scalable Laplacian K-modes
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 |
|---|---|---|---|
| Use assignment-derived modes (SLK-BO) instead of mean-shift mode updates (SLK-MS) (ablation) | SLK · Shuttle | NMI: 0.45 -> 0.51 | Helped |
| Use assignment-derived modes (SLK-BO) instead of mean-shift mode updates (SLK-MS) (ablation) | SLK · YTF | NMI: 0.82 -> 0.80 | Hurt |
| Use assignment-derived modes (SLK-BO) instead of mean-shift mode updates (SLK-MS) (ablation) | SLK · MNIST (code) | NMI: 0.88 -> 0.89 | Helped |
| Use assignment-derived modes (SLK-BO) instead of mean-shift mode updates (SLK-MS) (ablation) | SLK · LabelMe (GIST) | ACC: 0.72 -> 0.72 | No clear effect |
| Use assignment-derived modes (SLK-BO) instead of mean-shift mode updates (SLK-MS) (ablation) | SLK · YTF | ACC: 0.65 -> 0.64 | Hurt |
| Use the proposed concave-convex relaxation and bound optimizer rather than LK's convex relaxation | Laplacian K-modes · MNIST (small) | discrete-variable objective: 273.25 -> 67.09 | Helped |
| Use the proposed concave-convex relaxation and bound optimizer rather than LK's convex relaxation | Laplacian K-modes · LabelMe (Alexnet) | discrete-variable objective: -1.51384e+03 -> -1.80777e+03 | Helped |
Closest papers by meaning
- TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization (ICML 2025 · similarity 0.87)
- Nonlinear Laplacians: Tunable principal component analysis under directional prior information (NeurIPS 2025 · similarity 0.87)
- Dynamic Similarity Graph Construction with Kernel Density Estimation (ICML 2025 · similarity 0.87)
- Structure-Aware Spectral Sparsification via Uniform Edge Sampling (NeurIPS 2025 · similarity 0.87)
- Optimal Graph Clustering without Edge Density Signals (NeurIPS 2025 · similarity 0.85)
- Breaking the curse of dimensionality in structured density estimation (NeurIPS 2024 · similarity 0.85)
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.