TabSOM: A tabular-to-image encoding method based on self-organizing maps
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.g., t-SNE, UMAP, PCA). However, they encode only the marginal value of each feature and discard information about feature relationships. We propose TabSOM, a tabular-to-image encoding built on the Self-Organizing Map (SOM), which provides: (i) a spatial layout in which every input feature
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “TabSOM: A tabular-to-image encoding method based on self-org” ≈ “Tongyi-MAI/Z-Image-Turbo””
- PossiblePossibly related (embedding) · 47%Show HN: I implemented a neural network in SQL →
- LinkedLinked via arxiv author · 85%David Chushig-Muzo →
“TabSOM: A tabular-to-image encoding method based on self-organizing maps”
- LinkedLinked via arxiv author · 85%María Ángeles Rodríguez de Cara →
“TabSOM: A tabular-to-image encoding method based on self-organizing maps”
- LinkedLinked via arxiv author · 85%Eva Milara →
“TabSOM: A tabular-to-image encoding method based on self-organizing maps”
- LinkedLinked via arxiv author · 85%Francisco J. Lara-Abelenda →
“TabSOM: A tabular-to-image encoding method based on self-organizing maps”
- LinkedLinked via arxiv author · 85%Luis Zhinin-Vera →
“TabSOM: A tabular-to-image encoding method based on self-organizing maps”
- LinkedLinked via arxiv author · 85%Diego H. Peluffo-Ordóñez →
“TabSOM: A tabular-to-image encoding method based on self-organizing maps”