Research map / Learning theory and algorithms / Clustering
Leveraging Union of Subspace Structure to Improve Constrained Clustering
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 |
|---|---|---|---|
| Select pairwise queries using minimum subspace margin | SUPERPAC versus current state-of-the-art PCC algorithms · MNIST (10 digits; 500 pairwise queries) | classification error: about 20% -> 5% | Helped |
| Use pairwise constraints selected by SUPERPAC instead of unsupervised clustering | SUPERPAC · MNIST (10 digits; 500 pairwise queries) | classification error: 35% -> 5% | Helped |
| Use minimum-margin query selection rather than URASC-N query selection | SUPERPAC versus URASC-N · Yale and MNIST experiments | queries to achieve perfect clustering: roughly half the number of queries | Helped |
| Initialize certain sets with UoS-Explore (ablation) | SUPERPAC · Experiments across datasets | clustering performance | Helped |
Closest papers by meaning
- Neural Collaborative Subspace Clustering (ICML 2019 · similarity 0.90)
- A Critique of Self-Expressive Deep Subspace Clustering (ICLR 2021 · similarity 0.89)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.89)
- Subspace Clustering via Tangent Cones (NeurIPS 2017 · similarity 0.88)
- Probabilistic Temporal Subspace Clustering (CVPR 2017 · similarity 0.88)
- Multi-view Subspace Clustering on Topological Manifold (NeurIPS 2022 · similarity 0.88)
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.