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Discrete approach to machine learning

Machine Learning 2025-08-05 v1 Emerging Technologies Information Theory math.IT

Abstract

The article explores an encoding and structural information processing approach using sparse bit vectors and fixed-length linear vectors. The following are presented: a discrete method of speculative stochastic dimensionality reduction of multidimensional code and linear spaces with linear asymptotic complexity; a geometric method for obtaining discrete embeddings of an organised code space that reflect the internal structure of a given modality. The structure and properties of a code space are investigated using three modalities as examples: morphology of Russian and English languages, and immunohistochemical markers. Parallels are drawn between the resulting map of the code space layout and so-called pinwheels appearing on the mammalian neocortex. A cautious assumption is made about similarities between neocortex organisation and processes happening in our models.

Keywords

Cite

@article{arxiv.2508.00869,
  title  = {Discrete approach to machine learning},
  author = {Dmitriy Kashitsyn and Dmitriy Shabanov},
  journal= {arXiv preprint arXiv:2508.00869},
  year   = {2025}
}

Comments

preprint, 52 pages, 37 figures

R2 v1 2026-07-01T04:29:54.419Z