Research map / Learning theory and algorithms / Kernels and regression theory
Finding Relevant Information via a Discrete Fourier Expansion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Fourier-based SFFS feature selection with depth t=3 instead of other filter methods (except ReliefF) | feature selection on synthetic E1/E2 · E1, E2 | classification accuracy | Helped |
| Apply FOURIER-ORTH redundancy removal before feature selection (remove statistically redundant features) | SFFS · ALL AML, Isolet, USPS | number of non-trivial features: d-tilde 39 vs d 7128 on ALL AML; 309 vs 617 on Isolet | Helped |
| Increase depth parameter t from 1 to larger values (ablation) | SFFS · ALL AML, Covertype, Australian | classification accuracy | No clear effect |
| Increase depth parameter t beyond small values (ablation) | SFFS · real datasets (ALL AML, Covertype, Australian) | classification accuracy | Hurt |
| SFFS with t=1 or t=2 versus baselines (ReliefF, mRMR, MI, RFS, CCM) | feature selection on real datasets · USPS, Isolet, Musk, Australian, COIL20, Covertype, ALL AML | classification accuracy | Helped |
| Use SFFS (t=1) instead of mRMR for speed | feature selection running time · Covertype | running time (s): SFFS 2.7 s vs mRMR 1.41 s on Covertype; SFFS faster on Isolet/COIL20 (74.26 vs 3585 s for mRMR on Isolet vs COIL20 value | Mixed |
Closest papers by meaning
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- A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent (NeurIPS 2020 · similarity 0.87)
- Sharp Analysis of Random Fourier Features in Classification (AAAI 2022 · similarity 0.87)
- Large-Scale Learning with Fourier Features and Tensor Decompositions (NeurIPS 2021 · similarity 0.87)
- Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing (NeurIPS 2024 · similarity 0.86)
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