Improved SOM-Based High-Dimensional Data Visualization Algorithm
Bibliographic record
Abstract
In this paper, a new high-dimensional data visualization algorithm based on the Self-Organizing Map (SOM) is proposed. It is named TDSOM (three-dimensional self-organizing map) to describe its special characteristics. TDSOM trains the high-dimensional data with SOM network and projects it into particular point sets in the three-dimensional coordinate system. In the three-dimensional coordinate system, the x axis represents attributes of the original data set; the y axis represents the weight of each attribute; the z axis represents different categories of the mapping result. The most important is that researchers can watch the three-dimensional model from different viewpoints by rotating it and gain some interesting patterns. Through the experiment, TDSOM is proved to be much more accurate and more analytical than the traditional methods in displaying the high-dimensional data. The main innovation of the new TDSOM algorithm is the presentation of large data in three-dimensional coordinate system which provides a much wider view than the two-dimensional one. What’s more, users are able to discover some interesting patterns according to their own research areas through the model. The algorithm can be widely applied in areas such as data mining, pattern recognition and so on.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".