Most of our current data-analysis pipelines—statistical, neural, or otherwise—flatten or obscure deeper semantic structures. When you normalise tables or create embeddings, you often lose track of the “bridges” (shared entities), “pathways” (chains of relations), and “semantic neighbourhoods” that connect different datasets. Over the past year I’ve been exploring an alternative:...
This work establishes a new standard for analytical methods that refuse to sacrifice information for computational convenience, opening new possibilities for scientific discovery where perfect information preservation enables insights impossible with traditional lossy approaches. Key Features * Perfect Information Preservation: Zero reconstruction error across all domains (biological, textual, visual) vs....
Hi everyone, Over the past few months, I’ve been working on a new library and research paper that unify structure-preserving matrix transformations within a high-dimensional framework (hypersphere and hypercubes). Today I’m excited to share: MatrixTransformer a Python library and paper built around a 16-dimensional decision hypercube that enables smooth, interpretable...