Emerging-properties Mapping Using Spatial Embedding Statistics: EMUSES
It provides researchers with a tool to bridge predictive accuracy and interpretability for understanding complex phenomena, though it appears incremental as it builds on existing UMAP methods.
The paper tackles the problem of uncovering emergent properties from high-dimensional data by introducing EMUSES, which uses UMAP embeddings to detect latent structures, achieving high accuracy in predictions across datasets like handwritten digits, facial images, and brain disconnection data.
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.