Principal Sample Analysis for Data Reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it