Geometric Representations for High-Dimensional Data Using a Spherical SOFM
Bibliographic record
Abstract
The self-organizing feature map (SOFM) is primarily used to map high-dimensional data into low-dimensional spaces for pattern classification applications. The pre-defined connections in the SOFM lattice and the weight adaptation algorithm enable topological associations to emerge within arbitrary numeric data. The degree of association or similarity between neighboring nodes on the lattice is largely influenced by mathematical and statistical measures between the data vectors assigned to the nodes. The relationship between neighboring nodes, or cluster units, can be visually interpreted by an observer if this information is displayed as colors and/or distortions on the SOFM lattice. This paper describes how a SOFM that starts as a tessellated unit sphere can develop a closed surface topology of arbitrary N -dimensional data vectors that reflects information content as defined by the mathematical or statistical measure. Transforming the numeric data into a closed geometric form enables the information embedded in large high-dimensional data sets to be easily transferred into an immersive 3D virtual reality environment for interactive scientific data visualization. The implementation of the proposed methodology is illustrated using both high-dimensional synthetic data and the more common Fisher's Iris data.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".