Hierarchical Subspace Learning for Dimensionality Reduction to Improve\n Classification Accuracy in Large Data Sets
Bibliographic record
Abstract
Manifold learning is used for dimensionality reduction, with the goal of\nfinding a projection subspace to increase and decrease the inter- and\nintraclass variances, respectively. However, a bottleneck for subspace learning\nmethods often arises from the high dimensionality of datasets. In this paper, a\nhierarchical approach is proposed to scale subspace learning methods, with the\ngoal of improving classification in large datasets by a range of 3% to 10%.\nDifferent combinations of methods are studied. We assess the proposed method on\nfive publicly available large datasets, for different eigen-value based\nsubspace learning methods such as linear discriminant analysis, principal\ncomponent analysis, generalized discriminant analysis, and reconstruction\nindependent component analysis. To further examine the effect of the proposed\nmethod on various classification methods, we fed the generated result to linear\ndiscriminant analysis, quadratic linear analysis, k-nearest neighbor, and\nrandom forest classifiers. The resulting classification accuracies are compared\nto show the effectiveness of the hierarchical approach, reporting results of an\naverage of 5% increase in classification accuracy.\n
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".