Parallel coordinate order for<scp>high‐dimensional</scp>data
Bibliographic record
Abstract
Abstract Visualization of high‐dimensional data is counter‐intuitive using conventional graphs. Parallel coordinates are proposed as an alternative to explore multivariate data more effectively. However, it is difficult to extract relevant information through the parallel coordinates when the data are high‐dimensional with thousands of overlapping lines. The order of the axes determines the perception of information on parallel coordinates. Thus, the information between attributes remains hidden if coordinates are improperly ordered. Here we propose a general framework to reorder the coordinates. This framework is general enough to cover a wide range of data visualization objectives. It is also flexible enough to contain many conventional ordering measures. Consequently, we present the coordinate ordering binary optimization problem and enhance it to achieve a computationally efficient greedy approach that suits high‐dimensional data. Our approach is applied to wine data and genetic data. The purpose of dimension reordering of wine data is to highlight attributes' dependence. Genetic data are reordered to enhance cluster detection. The proposed framework shows that it is able to adapt the criteria for the visualization objective.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".