A mixture of common skew‐t factor analysers
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
Abstract A mixture of common skew‐ t factor analysers model is introduced for model‐based clustering of high‐dimensional data. By assuming common factors, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well modelled by the mixture of factor analysers model or a variant thereof. Furthermore, assuming that the component densities follow a skew‐ t distribution allows robust clustering of data with asymmetric clusters. This paper is the first time that skewed common factors have been used, and it marks an important step in robust clustering and classification of high‐dimensional data. The alternating expectation–conditional maximization algorithm is employed for parameter estimation. We demonstrate excellent clustering performance when our mixture of common skew‐ t factor analysers model is applied to real and simulated data. Copyright © 2014 John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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