Research map / Learning theory and algorithms / Matrix and tensor methods
Support Recovery in Sparse PCA with Incomplete Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the SDP relaxation of l1-regularized PCA on an incomplete, noisy matrix | Sparse PCA · Incomplete synthetic data | exact support recovery rate | Helped |
| Increase the spectral gap | SDP sparse PCA · Synthetic data, Experiment 2 | exact support recovery rate | Helped |
| Reduce the observation noise variance | SDP sparse PCA · Synthetic data, Experiment 2 | exact support recovery rate | Helped |
| Vary matrix dimension or support size | SDP sparse PCA · Synthetic data, Experiment 1 | exact support recovery rate | Mixed |
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