EBEK: exemplar-based kernel preserving embedding
Bibliographic record
Abstract
With the rapid increase in the available data, it becomes computationally harder to extract useful information. Thus, several techniques like PCA were proposed to embed high-dimensional data into low-dimensional latent space. However, these techniques don't take the data relations into account. This motivated the development of other techniques like MDS and LLE which preserve the relations between the data instances. Nonetheless, all these techniques still use latent features, which are difficult for data analysts to understand and grasp the information encoded in them. In this work, a new embedding technique is proposed to mitigate the previous problems by projecting the data to a space described by few points (i.e, exemplars) which preserves the relations between the data points. The proposed method Exemplar-based Kernel Preserving (EBEK) embedding is shown theoretically to achieve the lowest reconstruction error of the kernel matrix. Using EBEK in approximate nearest neighbor task shows its ability to outperform related work by up to 60% in the recall while maintaining a good running time. In addition, our interpretability experiments show that EBEK's selected basis are more understandable than the latent basis in images datasets.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".