Optimal Transport based Cross-Domain Integration for Heterogeneous Data.

Area of Science:

• Biomedical Sciences
• Data Science
• Machine Learning

Background:

• Detecting shared dynamic patterns across heterogeneous datasets is challenging due to data variability.
• Limited sample sizes in real-world experiments further complicate the identification of common dynamics.

Purpose of the Study:

• To propose a novel heterogeneous data integration framework using optimal transport.
• To extract shared patterns in conditional mean dynamics from diverse data sources.
• To enhance discriminative power by reducing irrelevant heterogeneity.

Main Methods:

• Optimal transport for aligning longitudinal data from heterogeneous datasets into a common latent space.
• Leveraging temporal dependencies within subjects for pattern extraction.
• Theoretical analysis including generalization error bounds and convergence rates.

Main Results:

• The framework effectively reduces heterogeneity unrelated to the signal.
• Improved prediction accuracy demonstrated on simulated and neuroscience data.
• Successful aggregation of information across diverse data sources and subjects.

Conclusions:

• The proposed optimal transport framework offers a powerful approach for integrating heterogeneous data.
• The method enhances the detection of shared dynamic patterns, particularly in biomedical applications.
• This approach provides a robust solution for analyzing complex, multi-domain datasets with limited samples.