A web-based interactive data visualization system for outlier subspace analysis
Bibliographic record
Abstract
Detecting outliers from high-dimensional data is a \nchallenge task since outliers mainly reside in various low dimensional subspaces of the data. To tackle this \nchallenge, subspace analysis based outlier detection \napproach has been proposed recently. Detecting outlying \nsubspaces in which a given data point is an outlier \nfacilitates a better characterization process for detecting \noutliers for high-dimensional data stream, and make \noutlier mining for large high-dimensional data set to be \nmore manageable. In this paper, to facilitate outlier \nsubspaces analysis from human perception perspectives in \nsupporting the development of efficient solutions for \nhigh-dimensional data, we propose a web-based \ninteractive data visualization system, which can display \nvarious low-dimensional outlier subspaces to allow users \nto observe and analyze the distributions of outliers. The \nproposed visualization tool can help the developers of \noutlier detection applications to directly examine the \ndistributions of outliers in various low-dimensional \nsubspaces to validate their experiment results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".