Context-Based Evaluation of Dimensionality Reduction Algorithms—Experiments and Statistical Significance Analysis
Bibliographic record
Abstract
Dimensionality reduction is a commonly used technique in data analytics. Reducing the dimensionality of datasets helps not only with managing their analytical complexity but also with removing redundancy. Over the years, several such algorithms have been proposed with their aims ranging from generating simple linear projections to complex non-linear transformations of the input data. Subsequently, researchers have defined several quality metrics in order to evaluate the performances of different algorithms. Hence, given a plethora of dimensionality reduction algorithms and metrics for their quality analysis, there is a long-existing need for guidelines on how to select the most appropriate algorithm in a given scenario. In order to bridge this gap, in this article, we have compiled 12 state-of-the-art quality metrics and categorized them into 5 identified analytical contexts. Furthermore, we assessed 15 most popular dimensionality reduction algorithms on the chosen quality metrics using a large-scale and systematic experimental study. Later, using a set of robust non-parametric statistical tests, we assessed the generalizability of our evaluation on 40 real-world datasets. Finally, based on our results, we present practitioners’ guidelines for the selection of an appropriate dimensionally reduction algorithm in the present analytical contexts.
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".