Research map / Learning theory and algorithms / Matrix and tensor methods
Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use QCQP-based clustering instead of Denoised GPCA | Subspace clustering · Synthetic, n=3, subspace dimensions [2 2], ǫ=0.10 | mean worst-case fitting error (errf): µ = 0.1015 -> µ = 0.0992 | Helped |
| Use QCQP-based clustering instead of Denoised GPCA | Subspace clustering · Synthetic, n=3, subspace dimensions [2 2], ǫ=0.15 | mean worst-case fitting error (errf): µ = 0.1608 -> µ = 0.1485 | Helped |
| Use QCQP-based clustering instead of Denoised GPCA | Subspace clustering · Synthetic, n=3, subspace dimensions [2 2], ǫ=0.20 | mean worst-case fitting error (errf): µ = 0.2020 -> µ = 0.1978 | Helped |
| Use QCQP-based clustering instead of Denoised GPCA | Subspace clustering · Synthetic, n=4, subspace dimensions [3 3], ǫ=0.15 | mean worst-case fitting error (errf): µ = 0.2058 -> µ = 0.1481 | Helped |
| Use QCQP-based clustering instead of LRR | Subspace clustering · Synthetic, n=2, subspace dimensions [1 1 1], ǫ=0.10 | mean worst-case fitting error (errf): µ = 0.1021 -> µ = 0.0993 | Helped |
| Use QCQP-based clustering with outlier handling instead of LRR | Subspace clustering · Synthetic with outliers, n=2, subspace dimensions [1 1 1], ǫ=0.10 | mean worst-case fitting error (errf): µ = 0.2223 -> µ = 0.0996 | Helped |
| Use QCQP-based clustering instead of Denoised GPCA | Planar segmentation · Merton I | misclassification rate: 49.12 (±3.91) -> 4.29 (±3.45) | Helped |
| Use QCQP-based clustering instead of Denoised GPCA | Planar segmentation · Merton II | misclassification rate: 49.58 (±0.66) -> 2.33 (±2.85) | Helped |
| Use QCQP-based clustering instead of Denoised GPCA | Planar segmentation · Wadham | misclassification rate: 49.74 (±0.92) -> 1.33 (±2.19) | Helped |
| Tune LRR’s λ parameter | LRR planar segmentation · Merton I | misclassification rate: 3.33% | Mixed |
| Tune SSC’s τ parameter | SSC planar segmentation · Merton I | misclassification rate: 5% | Mixed |
Closest papers by meaning
- SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms (CVPR 2018 · similarity 0.91)
- Stochastic Sparse Subspace Clustering (CVPR 2020 · similarity 0.90)
- Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (CVPR 2016 · similarity 0.89)
- Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling (CVPR 2019 · similarity 0.89)
- Hyperplane Clustering via Dual Principal Component Pursuit (ICML 2017 · similarity 0.89)
- Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap (NeurIPS 2022 · similarity 0.89)
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