Research map / Learning theory and algorithms / Matrix and tensor methods
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace spectral clustering on the n×n representation with k-means on V (fully online OLRSC-F) (ablation) | OLRSC · Mushrooms, DNA, Protein, USPS, MNIST-20K | clustering accuracy (%) | Mixed |
| Use OLRSC (online basis-dictionary LRR) instead of ORPCA for subspace clustering | OLRSC · USPS | clustering accuracy (%): +10.25 points (65.95 vs 55.70) | Helped |
| Use OLRSC instead of SSC on the Protein dataset | OLRSC · Protein | clustering accuracy (%): 43.30 vs 44.27 | Hurt |
| Use OLRSC instead of LRR on MNIST-20K | OLRSC · MNIST-20K | clustering accuracy (%): 57.74 vs 55.23 | Helped |
Closest papers by meaning
- Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (CVPR 2016 · similarity 0.88)
- High-Rank Matrix Completion and Clustering under Self-Expressive Models (NeurIPS 2016 · similarity 0.88)
- Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming (CVPR 2016 · similarity 0.88)
- Sparse Subspace Clustering by Learning Approximation ℓ0 Codes (AAAI 2017 · similarity 0.88)
- From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement (NeurIPS 2024 · similarity 0.86)
- Exploring a Principled Framework for Deep Subspace Clustering (ICLR 2025 · similarity 0.86)
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