Robust space transformations for distance-based operations
Bibliographic record
Abstract
For many KDD operations, such as nearest neighbor search, distance-based clustering, and outlier detection, there is an underlying κ-D data space in which each tuple/object is represented as a point in the space. In the presence of differing scales, variability, correlation, and/or outliers, we may get unintuitive results if an inappropriate space is used.The fundamental question that this paper addresses is: "What then is an appropriate space?" We propose using a robust space transformation called the Donoho-Stahel estimator. In the first half of the paper, we show the key properties of the estimator. Of particular importance to KDD applications involving databases is the stability property, which says that in spite of frequent updates, the estimator does not: (a) change much, (b) lose its usefulness, or (c) require re-computation. In the second half, we focus on the computation of the estimator for high-dimensional databases. We develop randomized algorithms and evaluate how well they perform empirically. The novel algorithm we develop called the Hybrid-random algorithm is, in most cases, at least an order of magnitude faster than the Fixed-angle and Subsampling algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".