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Hyperdimensional connection method - A Lossless Framework Preserving Meaning, Structure, and Semantic Relationships across Modalities.(A MatrixTransformer subsidiary)

by fikayoAy
18th Jul 2025
1 min read
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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. 0.1% loss in traditional methods
  • Cross-Modal Pattern Discovery: Unique ability to identify relationships across different feature representation types (3,015 connections in MNIST vs. 0 for traditional methods)
  • Semantic Coherence Quantification: Achieves 94.7% semantic coherence in text analysis with queryable connection structures
  • Domain-Agnostic Performance: Consistent advantages across 784-dimensional visual data, high-dimensional biological matrices, and multi-modal text representations
  • 100% Sparsity Preservation: Maintains complete matrix sparsity while traditional dense methods achieve 0%

Experimental Validation

Comprehensive benchmarking across three diverse domains:

  1. Biological Data: Drug-gene interaction networks preserving clinically relevant patterns (NFE2L2, AR, CYP3A4)
  2. Textual Data: NewsGroups dataset with 23 cross-matrix links enabling multi-modal semantic analysis
  3. Visual Data: MNIST digit recognition with cross-digit relationship discovery and geometric pattern analysis

Technical Innovation

  • Hyperdimensional Connection Discovery: Identifies meaningful relationships in 8-dimensional hyperdimensional space
  • Hypersphere Projection: Constrains matrices to hypersphere surfaces while preserving structural properties
  • Bidirectional Matrix Conversion: Enables lossless round-trip transformation between connection and matrix representations
  • Query-Ready Architecture: Supports unlimited post-hoc analysis including similarity searches, anomaly detection, and relationship discovery

Applications

  • Bioinformatics: Drug discovery with preserved biological network structure
  • Natural Language Processing: Multi-modal text analysis with cross-representation relationship discovery
  • Computer Vision: Visual pattern analysis with cross-pattern relationship discovery
  • Financial Analysis: Anomaly detection preserving sparse transaction patterns
  • Scientific Computing: Simulation embeddings maintaining physical constraints

Repository Contents

  • Complete MatrixTransformer implementation with hyperdimensional extensions
  • Experimental benchmarking code and datasets
  • Comprehensive visualizations and analysis tools
  • Domain-specific applications and examples
  • Full reproducibility documentation

Clone from github and Install from wheel file

git clone https://github.com/fikayoAy/MatrixTransformer.git

cd MatrixTransformer

pip install dist/matrixtransformer-0.1.0-py3-none-any.whl

Links:

- Research Paper (Hyperdimensional Module): [Zenodo DOI](https://doi.org/10.5281/zenodo.16051260)

Parent Library – MatrixTransformer: [GitHub](https://github.com/fikayoAy/MatrixTransformer)

MatrixTransformer Core Paper: [https://doi.org/10.5281/zenodo.15867279](https://doi.org/10.5281/zenodo.15867279)

Would love to hear thoughts, feedback, or questions. Thanks!