Research map / Learning theory and algorithms / Matrix and tensor methods
Complex Moment-Based Supervised Eigenmap for Dimensionality Reduction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use K-CMSE instead of K-RR | K-CMSE · SMK-CAN-187 | ACC (%): 68.71±2.69 -> 71.89±2.48 | Helped |
| use K-CMSE instead of K-LPP | K-CMSE · TOX-171 | ACC (%): 50.92±2.69 -> 96.50±1.48 | Helped |
| use K-CMSE instead of K-LFDA | K-CMSE · GLI-85 | ACC (%): 85.19±2.90 -> 86.73±2.83 | Helped |
| use K-CMSE instead of K-RR | K-CMSE · pixraw10P | ACC (%): 97.00±2.02 -> 97.00±1.45 | No clear effect |
| use K-CMSE instead of K-RR | K-CMSE · MNIST | ACC (%): 95.55±0.47 -> 95.70±0.33 | Helped |
| use K-CMSE instead of K-RR | K-CMSE · GLI-85 | NMI: 0.48±0.11 -> 0.47±0.11 | Hurt |
| use K-CMSE instead of K-RR | K-CMSE · att644 | ACC (%): 86.25±1.24 -> 86.50±1.28 | Helped |
| combine two objective functions (mu near 1 uses only one objective) (ablation) | K-CMSE · Artificial problem | ACC | Helped |
| widen region Omega and increase L (more eigenvectors in complex moment subspace) (ablation) | K-CMSE · Artificial problem | ACC | Helped |
| make Omega overlarge (ablation) | K-CMSE · Artificial problem | ACC | Hurt |
| vary k of k-nearest neighbors above 10 | K-CMSE vs existing methods · Artificial problem | ACC | Helped |
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