Class-Specific Joint Feature Screening in Ultrahigh-Dimensional Mixture Regression
Bibliographic record
Abstract
Finite mixture of regression models are ubiquitous for analyzing complex data. They aim to detect heterogeneity in the effects of a set of features on a response over a finite number of latent classes. When the number of features is large, a direct fitting of mixture regressions can be computationally infeasible and often leads to a poor interpretative value. One practical strategy is to screen out most irrelevant features before an in-depth analysis. In this paper, we propose a novel method for feature screening in ultrahigh-dimensional Gaussian finite mixture of regressions. The new method is built upon a sparsity-restricted expectation-approximation-maximization algorithm, which simultaneously removes varying sets of irrelevant features from multiple latent classes. In the screening process, joint effects between features are naturally accounted and class-specific screening results are produced without ad hoc steps. These merits give the new method an edge to outperform the existing screening methods. The promising performance of the method is supported by both theory and numerical examples including a real data analysis. Supplementary materials for this article are available online.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".