Research map / Learning theory and algorithms / Kernels and regression theory
Random Features for Sparse Signal Classification
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply random features to compressive measurements rather than original signals | Random-feature SVM · MNIST, 8 scene categories, INRIA person, CIFAR-10, and Street view house numbers | accuracy | No clear effect |
| Use compressive measurements before random-feature construction | Random-feature construction · Datasets in Figures 2–6 | construction time | Helped |
| Use few random features | Compressive and typical random-feature SVMs · Some datasets | accuracy | Hurt |
| Increase the number of random features | Compressive and typical random-feature SVMs · Datasets in Figures 2–6 | accuracy | Helped |
| Replace original data with compressive measurements | Linear SVM · Experiments across five datasets | accuracy | Hurt |
Closest papers by meaning
- Learning Kernels with Random Features (NeurIPS 2016 · similarity 0.87)
- Feature Adaptation for Sparse Linear Regression (NeurIPS 2023 · similarity 0.87)
- Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels (NeurIPS 2018 · similarity 0.87)
- Sharp Analysis of Random Fourier Features in Classification (AAAI 2022 · similarity 0.87)
- Gaussian Quadrature for Kernel Features (NeurIPS 2017 · similarity 0.87)
- Learning Adaptive Random Features (AAAI 2019 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.