Research map / Learning theory and algorithms / Matrix and tensor methods
High-Rank Matrix Completion and Clustering under Self-Expressive Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Lifting-based group-sparse SSC with rank-one constraints (SSC-Lifting) instead of low-rank completion followed by clustering | high-rank synthetic and CMU Mocap data · high-rank synthetic (n=100, L=12, d=10) and CMU Mocap | completion error | Helped |
| Lifting-based SSC-Lifting instead of low-rank completion followed by SSC | low-rank motion segmentation (Hopkins 155) · Hopkins 155 | clustering error | No clear effect |
| Column-wise expectation completion (SSC-CEC) baseline versus SSC-Lifting | motion segmentation with missing entries · Hopkins 155 | clustering error | Hurt |
| Rank-one (lifting) constraint removed versus kept, nuclear-norm relaxation weight gamma varied (ablation) | SSC-Lifting · synthetic high-rank | stable rank of blocks A_i | No clear effect |
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