Emerging-properties Mapping Using Spatial Embedding Statistics: EMUSES

TL;DR

EMUSES is a novel method that uses UMAP-based high-dimensional embeddings to uncover and interpret emergent properties in complex, multifactorial datasets, enhancing understanding and prediction of such phenomena.

Contribution

This paper introduces EMUSES, a new approach combining UMAP embeddings with statistical analysis to detect and interpret emergent properties in high-dimensional data.

Findings

Successfully applied to diverse datasets including handwritten digits, face images, and brain disconnection data.

Achieves high accuracy in predicting emergent outcomes.

Provides visual and statistical insights into complex data interactions.

Abstract

Understanding complex phenomena often requires analyzing high-dimensional data to uncover emergent properties that arise from multifactorial interactions. Here, we present EMUSES (Emerging-properties Mapping Using Spatial Embedding Statistics), an innovative approach employing Uniform Manifold Approximation and Projection (UMAP) to create high-dimensional embeddings that reveal latent structures within data. EMUSES facilitates the exploration and prediction of emergent properties by statistically analyzing these latent spaces. Using three distinct datasets--a handwritten digits dataset from the National Institute of Standards and Technology (NIST, E. Alpaydin, 1998), the Chicago Face Database (Ma et al., 2015), and brain disconnection data post-stroke (Talozzi et al., 2023)--we demonstrate EMUSES' effectiveness in detecting and interpreting emergent properties. Our method not only predicts outcomes with high accuracy but also provides clear visualizations and statistical insights into the underlying interactions within the data. By bridging the gap between predictive accuracy and interpretability, EMUSES offers researchers a powerful tool to understand the multifactorial origins of complex phenomena.