MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization

Area of Science:

• Data Science
• Machine Learning
• Computer Vision

Background:

• Dimensionality reduction (DR) is vital for simplifying high-dimensional data in fields like data engineering and visualization.
• Traditional DR methods often struggle to balance accuracy with explainability, a critical need in image, tabular, and text data analysis.
• Existing techniques present a trade-off between performance and transparency, limiting their effectiveness in real-world applications.

Purpose of the Study:

• To introduce a novel dimensionality reduction method, MoE-based Explainable Deep Manifold Transformation (DMT-ME), addressing the accuracy-explainability challenge.
• To enhance DR accuracy and model transparency for complex, high-dimensional datasets.
• To provide a robust solution for data analysis requiring both precise and interpretable results.

Main Methods:

• The proposed DMT-ME combines a geometry-aware hyperbolic mapper with Mixture of Experts (MoE) models.
• MoE facilitates sparse expert specialization for representational gains, while the hyperbolic component refines structurally complex data.
• The approach leverages MoE-based sparse routing and structure-aware matching for enhanced DR accuracy and explainability.

Main Results:

• DMT-ME demonstrates superior performance in both dimensionality reduction accuracy and model explainability compared to traditional methods.
• The MoE structure explicitly links input data, embedding outcomes, and key features, improving transparency.
• Experiments confirm DMT-ME's effectiveness across various data types, including image, tabular, and text data.

Conclusions:

• DMT-ME offers a robust solution for complex data analysis by effectively balancing DR accuracy and explainability.
• The method provides enhanced transparency by clearly mapping data relationships within the embedding space.
• The developed approach represents a significant advancement for interpretable machine learning and data visualization.