Research map / Learning theory and algorithms / Matrix and tensor methods
Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the full feature matrix with the DRLS-selected column subset for ridge regression | Ridge regression on LGG multi-omic data · LGG simulated outcomes, σ²=10^-3, 10^0, and 10^3 | risk ratio R(yhat_C) / R(yhat_A): 0.99 | No clear effect |
| Select columns deterministically by ridge leverage score, retaining 1512 columns | LGG multi-omic feature matrix · LGG multi-omic data, k=3, ε=0.1 | selected columns: 1512 | Helped |
| Use DRLS column sampling for rank-k projection-cost preservation | DRLS · Any rank k orthogonal projection | projection cost | Helped |
| Use DRLS column sampling for column subset selection | DRLS · Rank-k matrix approximation | squared Frobenius approximation error | Helped |
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