Clustering
Self-Organizing Map
The Self-Organizing Map (SOM), commonly also known as Kohonen network (Kohonen 1982, Kohonen 2001) is a computational method for the visualization and analysis of high-dimensional data, especially experimentally acquired information.
Extracted from scholarpedia
Sources:
Image: scholarpedia
Book: Self-Organizing Maps
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Clustering | 21 | 16.54% |
| Quantization | 10 | 7.87% |
| General Classification | 10 | 7.87% |
| Classification | 6 | 4.72% |
| Time Series Analysis | 6 | 4.72% |
| Dimensionality Reduction | 5 | 3.94% |
| Image Classification | 4 | 3.15% |
| BIG-bench Machine Learning | 4 | 3.15% |
| Continual Learning | 2 | 1.57% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
