Bibliographic record
Abstract
Data reduction is an essential technique used for purifying data, training discriminative models more efficiently, encouraging generalizability, and for using less storage space for memory-limited systems. The literature on data reduction focuses mostly on dimensionality reduction, however, data sample reduction (i.e. removal of data points from a dataset) has its own benefits and is no less important given growing sizes of datasets and the growing need for usable data analysis methods on the network edge. This paper proposes a new data sample reduction method, Principal Sample Analysis (PSA), which reduces the number (population) of data samples as a preprocessing step for classification. PSA ranks the samples of each class considering how well they represent it and enables better discriminative learning by using the sparsity and similarity of samples at the same time. Data sample reduction then occurs by cutting off the lowest ranked samples. The PSA method can work alongside any other data reduction/expansion and classification method. Experiments are carried out on three datasets (WDBC, AT&T, and MNIST) with contrasting characteristics and show the state-of-the-art effectiveness of the proposed method.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".