Research map / Learning theory and algorithms / Matrix and tensor methods
Efficient Top-K Feature Selection Using Coordinate Descent Method
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CD-LSR instead of RPFS to optimize the l2,0-constrained LSR | LSR feature selection · SRBCT, k=1, k=5, and k=10 | converged objective function value: 49.08±3.5 -> 45.71±4.1; 29.81±9.2 -> 12.91±2.1; 12.28±2.5 -> 6.994±1.0 | Helped |
| Use CD-LSR instead of RPFS to optimize the l2,0-constrained LSR | LSR feature selection · JAFFE, k=1, k=5, and k=10 | converged objective function value: 180.0±2.7 -> 175.4±1.9; 124.2±4.9 -> 118.0±7.1; 74.07±4.8 -> 68.36±8.5 | Helped |
| Use CD-LSR instead of IPFS | l2,0-constrained LSR · JAFFE, k=5 | CPU time in second: 0.477±0.03 -> 0.342±0.01 | Helped |
| Use CD-LSR instead of RPFS | l2,0-constrained LSR · USPS, k=5 | CPU time in second: 3.114±0.31 -> 3.376±0.66 | Hurt |
| Use CD-LSR instead of IPFS | l2,0-constrained LSR · SRBCT, k=5 | CPU time in second: 1.761±0.02 -> 5.913±0.07 | Hurt |
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