Constrained Generative Adversarial Learning for Dimensionality Reduction
Bibliographic record
Abstract
Emerging data-driven technologies and big data analytics generate and deal with high-dimensional data. Transformation of such data into a low-dimensional feature space brings about numerous benefits, such as a more discriminant feature space, performance enhancement, less computational burden, and facilitating data visualization. This paper proposes a novel dimensionality reduction algorithm based on generative adversarial networks to tackle the issues related to high-dimensional data and common challenges in dimensionality reduction. To this aim, two constraints are defined to preserve the characteristics of the original data while rectifying the data distribution upon transformation. Formulating the transformation as sequential projections, the proposed Constrained Adversarial Dimensionality Reduction (CADR) method finds a set of sequential projection vectors that lead to a feature space in which between-class separability and within-class integrity are satisfied. This is while the transformed data perfectly comply with the pairwise affinity correlation in the original feature space. To evaluate the proposed method, nine advanced dimensionality reduction techniques are employed to enable a comparative study. The experiments are performed on several real-world benchmark datasets in terms of classification accuracy, F-measure, and G-mean. The obtained results show that the CADR could yield classification performance at a satisfactory level and outperforms the other competitors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".