Unsupervised feature selection for proportional data clustering via expectation propagation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, an expectation propagation (EP) inference framework for unsupervised feature selection is proposed for modeling proportional data which naturally appear in many applications such as text and image modeling, in the context of finite mixture-based clustering. Within our framework, simultaneous clustering and feature selection is formalized using finite mixtures of generalizing Dirichlet (GD) distributions. The proposed EP-based inference framework allows to obtain a full posterior distribution on all our unsupervised feature selection model's parameters. Moreover, the complexity of the deployed mixture models and all the involved model parameters can be evaluated simultaneously. The effectiveness and efficiency of the proposed algorithm are evaluated on both synthetic data and two challenging applications namely human action videos categorization and facial expression recognition.
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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 | 0.000 | 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 | 0.002 |
| 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