Supervised <i>t</i>-SNE for Metric Learning With Stochastic and Geodesic Distances
Why this work is in the frame
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Bibliographic record
Abstract
The t-distributed stochastic neighbor embedding (t-SNE) consists of a powerful algorithm for visualizing high-dimensional data in a lower dimensional space. It is extensively employed in machine learning (ML) and data analysis, including unsupervised metric learning. In this article, we propose improvements concerning two main aspects of the t-SNE. First, the incorporation of class labels is adopted to increase its suitability for supervised classification. Second, stochastic and geodesic distances are used as dissimilarity measures to avoid the dependence of the standard Euclidean distance, which is particularly sensitive to outliers. Computational experiments with several real-world datasets indicate that the proposed methodological approach is capable of improving classification accuracy compared with established methods. The results indicate a superior performance compared with the regular t-SNE and linear discriminant analysis (LDA), and a dependence on fewer parameters in comparison with the state-of-the-art supervised uniform manifold approximation and projection (UMAP) algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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