Research map / Learning theory and algorithms / Prediction and decision losses
Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Lasso sparsity penalty (lambda > 0) vs ridge-less case (lambda = 0) on sparse-mean Gaussian mixture (ablation) | Lasso-based classification (ISTA) · Synthetic Gaussian mixture, p=100, sparsity alpha=0.9 | relative accuracy gain over ridge-less classifier | Mixed |
| Adding sparsity (Lasso penalty) with appropriately chosen lambda (ablation) | Lasso-based classification on sparse-mean data · Synthetic Gaussian mixture, high sparsity alpha=0.9 vs ridge-less | classification accuracy | Helped |
| Choosing lambda by grid search on theoretical classification error instead of cross-validation | Lasso-based classification hyperparameter selection · MNIST, Amazon review, CIFAR-10 (PCA/tf*idf) | classification error | No clear effect |
| Using the theoretical ISTA iteration (Algorithm 1) instead of empirical ISTA for computing the solution | Lasso classification solver · Empirical comparison (appendix) | convergence speed (iterations and running time) | Helped |
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