Generalized dirichlet mixture matching projection for supervised linear dimensionality reduction of proportional data
Bibliographic record
Abstract
In this paper, a novel effective method to reduce the dimensionality of labeled proportional data is introduced. Most well-known existing linear dimensionality reduction methods rely on solving the generalized eigen value problem which fails in certain cases such as sparse data. The proposed algorithm is a linear method and uses a novel approach to the problem of dimensionality reduction to solve this problem while resulting higher classification rates. Data is assumed to be from two different classes where each class is matched to a mixture of generalized Dirichlet distributions after projection. Jeffrey divergence is then used as a dissimilarity measure between the projected classes to increase the inter-class variance. To find the optimal projection that yields the largest mutual information, genetic algorithm is used. The method is especially designed as a preprocessing step for binary classification, however, it can handle multi-modal data effectively due to the use of mixture models and therefore can be used for multi-class problems as well.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".