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

fikayoAy

Rejected for the following reason(s):

No LLM generated, heavily assisted/co-written, or otherwise reliant work.
Difficult to evaluate, with potential yellow flags.
Insufficient Quality for AI Content.

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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:

Biological Data: Drug-gene interaction networks preserving clinically relevant patterns (NFE2L2, AR, CYP3A4)
Textual Data: NewsGroups dataset with 23 cross-matrix links enabling multi-modal semantic analysis
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!

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