Robust Reduced Rank Mixture Discriminant Analysis
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT In the case of a large number of feature vector variables, using multivariate Gaussian mixture models, discrimination in a reduced subspace is studied, generalizing Hastie and Tibshirani's (1996) work, to a situation in which the outliers are present in the data. In the case of the Gaussian Mixture models, the reduced rank discriminant analysis is equivalent to the weighted rank k linear discriminant analysis (LDA). The reduced rank solution in the mixtures of multivariate Gaussian models was obtained from the full rank robust mixture solution. The classification in the new dimensions was compared with the discriminant analysis approach based on the original coordinates, using robust S-estimators. In most of the cases, the robust reduced rank mixture discriminant analysis (mda) performed better for the test data. However, for the case of common component covariance being diagonal, the robust reduced rank mixture discriminant analysis performed better than the robust full rank mixture discriminant analysis producing smaller errors in classification.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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