High-dimensional unsupervised classification via parsimonious\n contaminated mixtures
Bibliographic record
Abstract
The contaminated Gaussian distribution represents a simple heavy-tailed\nelliptical generalization of the Gaussian distribution; unlike the\noften-considered t-distribution, it also allows for automatic detection of mild\noutlying or "bad" points in the same way that observations are typically\nassigned to the groups in the finite mixture model context. Starting from this\ndistribution, we propose the contaminated factor analysis model as a method for\ndimensionality reduction and detection of bad points in higher dimensions. A\nmixture of contaminated Gaussian factor analyzers (MCGFA) model follows\ntherefrom, and extends the recently proposed mixture of contaminated Gaussian\ndistributions to high-dimensional data. We introduce a family of 32\nparsimonious models formed by introducing constraints on the covariance and\ncontamination structures of the general MCGFA model. We outline a variant of\nthe expectation-maximization algorithm for parameter estimation. Various\nimplementation issues are discussed, and the novel family of models is compared\nto well-established approaches on both simulated and real data.\n
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".