Show HN: Lossless Semantic Matrix Analysis (99.999% accuracy, no training)
I built an open-source system for lossless, structure-preserving analysis of matrix data.
It auto-discovers semantic relationships between datasets (e.g., drugs ↔ genes ↔ categories) with 99.999% accuracy — without deep learning or lossy compression.
It supports CSV/TSV/Excel data, runs via Docker, and outputs semantic clusters, property stats, similarity scores, and visualizations.
No setup needed. Works on any data. Fully explainable.
It auto-discovers semantic relationships between datasets (e.g., drugs ↔ genes ↔ categories) with 99.999% accuracy — without deep learning or lossy compression.
It supports CSV/TSV/Excel data, runs via Docker, and outputs semantic clusters, property stats, similarity scores, and visualizations.
No setup needed. Works on any data. Fully explainable.
GitHub: https://github.com/fikayoAy/MatrixTransformer Docker: fikayomiayodele/hyperdimensional-connection
Happy to answer any questions!