arxiv_ml 75% Match Research Paper Machine learning engineers,Data scientists,Researchers in recommendation systems 1 week ago

Modular Linear Tokenization (MLT)

large-language-models › model-architecture

📄 Abstract

Abstract: This paper introduces Modular Linear Tokenization (MLT), a reversible and deterministic technique for encoding high-cardinality categorical identifiers into compact numerical vectors. Unlike traditional hashing or one-hot encodings, MLT preserves bijective mappings by leveraging modular arithmetic over finite fields and invertible linear transformations. The method offers explicit control of dimensionality and computational scalability while maintaining full reversibility, even for millions of identifiers. Experimental results on the MovieLens 20M dataset show that MLT achieves comparable predictive performance to supervised embeddings while requiring significantly fewer parameters and lower training cost. An open-source implementation of MLT is available on PyPI (https://pypi.org/project/light-mlt/) and GitHub (https://github.com/tcharliesschmitz/light-mlt).

Authors (1)

Tcharlies Schmitz

Submitted

October 29, 2025

arXiv Category

cs.LG

arXiv PDF Code

Key Contributions

Introduces Modular Linear Tokenization (MLT), a reversible and deterministic technique for encoding high-cardinality categorical identifiers into compact numerical vectors. MLT uses modular arithmetic and invertible linear transformations, offering explicit control over dimensionality and scalability while maintaining full reversibility.

Business Value

Enables more efficient processing and modeling of large-scale categorical data, reducing memory footprint and computational costs in applications like recommendation systems and feature engineering.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as it's a preprocessing technique that can be integrated into existing pipelines.

Limitations Addressed

High dimensionality and parameter count of traditional embeddings (e.g., one-hot, learned embeddings),Loss of information or non-reversibility in hashing,Computational scalability issues with millions of identifiers

Performance Gains

Achieves comparable predictive performance to supervised embeddings while requiring significantly fewer parameters and lower training cost.

View Code on GitHub

Technical Tags

TokenizationCategorical FeaturesReversible EncodingModular ArithmeticLinear TransformationsHigh-Cardinality FeaturesEmbeddingsDimensionality Control

Research Topics

Efficient encoding of categorical dataReversible feature transformationAlternatives to hashing and one-hot encodingScalable embedding techniques

Methods & Architectures

Modular Linear Tokenization (MLT)Modular arithmeticInvertible linear transformationsFinite field arithmetic MLPEmbedding Layers

Applications & Tasks

Data Preprocessing Machine Learning Recommendation Systems Inefficiency of hashing and one-hot encoding for high-cardinality featuresLoss of information in traditional encoding methodsHigh dimensionality and parameter count of embeddings Encoding high-cardinality categorical identifiersCreating compact numerical representationsEnabling reversible feature transformation

Datasets & Benchmarks

Datasets

MovieLens 20M

Related Fields

Machine LearningData PreprocessingFeature EngineeringRecommendation SystemsInformation TheoryLinear Algebra

Keywords

TokenizationCategorical FeaturesReversible EncodingModular ArithmeticLinear TransformationsHigh-CardinalityEmbeddingsDimensionality ReductionMovieLensRecommendation SystemsFeature EngineeringData Preprocessing

Academic Context

#Efficient encoding of categorical data#Reversible feature transformation#Alternatives to hashing and one-hot encoding#Scalable embedding techniques

Technology Stack

Frameworks & Libraries

PyPI (light-mlt)

Programming Languages

Python

Commercial Potential

Potential Products

Efficient feature encoding librariesData preprocessing tools for large-scale categorical data

Target Industries

E-commerceMediaAdvertisingTechnology

Use Case Examples

Encoding user IDs and item IDs in recommendation systemsRepresenting high-cardinality features in tabular data modelsEfficiently handling categorical variables in large datasets

Competitive Edge

Offers a reversible, deterministic, and parameter-efficient alternative to traditional methods like hashing and learned embeddings for high-cardinality categorical features.

Market Opportunity

Large market for efficient data processing and feature engineering tools.

Revenue Models

Licensing of the libraryintegration into commercial ML platforms.

Resource Requirements

Compute Needs

Low to moderate, efficient encoding and decoding.

Data Requirements

Datasets with high-cardinality categorical features.

Deployment Constraints

Requires careful selection of parameters (e.g., field size, transformation matrix) based on data characteristics.

Scalability

Designed for scalability with millions of identifiers.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for widespread adoption in libraries.

Licensing

Open Source (MIT License implied by GitHub)

Patent Potential

Moderate, for the specific MLT algorithm and its application.

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