Research map / Learning theory and algorithms / Matrix and tensor methods
Generalized Leverage Scores: Geometric Interpretation and Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select columns using generalized leverage scores for a chosen singular-vector subset | GCSS · Arbitrary target matrix B | projected target Frobenius norm: \|CC^+B\|_F^2 \geq (1-\epsilon)(1-\delta)\|B\|_F^2 | Helped |
| Select columns from both matrices using generalized leverage scores | Sparse CCA · Paired column subsets of A and B | sum of squared canonical correlations: \|W^TW'\|_F^2 \geq (1-\epsilon)^2(1-\delta)^2\|Q_A^TQ_B\|_F^2 | Helped |
| Use generalized-leverage-score selection instead of Greedy | GLS for GCSS · Real feature-selection datasets | objective ratio | Hurt |
| Use generalized-leverage-score selection instead of Greedy | GLS for GCSS · Real feature-selection datasets, large k | running time | Helped |
| Use generalized-leverage-score selection when the scores are uninformative | GLS for GCSS · Some real datasets | objective ratio | Hurt |
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