Robust feature selection via nonconvex sparsity-based methods
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a new model for supervised multiclass feature selection which has the 2,1 -norm in both the fidelity loss and the regularization terms with an additional 2,0 -constraint.This problem is challenging for applying available optimization methods because of the discontinuous and nonconvex nature of the 2,0 -norm.We first convert the constraint defined by the 2,0 -norm into a new constraint defined by a difference of two matrix norms.Then we reformulate the problem as an unconstrained problem using the exact penalty method.Based on a derived formula for the proximal mapping of this difference of matrix norms and Nesterov's smoothing techniques, the nonmonotonic accelerated proximal gradient method is applied to solve the unconstrained problem.Numerical experiments are conducted on many benchmark data sets to show the effectiveness of our proposed method in comparison with existing methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it