Sparse Orthogonal Linear Discriminant Analysis
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Bibliographic record
Abstract
In this paper, sparse orthogonal linear discriminant analysis (OLDA) is studied. The main contributions of the present work include the following: (i) all minimum Frobenius-norm/dimension solutions of the optimization problem used for establishing OLDA are characterized explicitly; and (ii) this explicit characterization leads to two numerical algorithms for computing a sparse linear transformation for OLDA. The first is based on the gradient flow approach while the second is a sequential linear Bregman method. We experiment with real world datasets to illustrate that the sequential linear Bregman method is much better than the gradient flow approach. The sequential linear Bregman method always achieves comparable classification accuracy with the normal OLDA, satisfactory sparsity and orthogonality, and acceptable CPU times.
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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 | 0.001 | 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