Emerging-properties Mapping Using Spatial Embedding Statistics: EMUSES
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Foulon, Chris Ovando-Tellez, Marcela Talozzi, Lia Corbetta, Maurizio Matsulevits, Anna de Schotten, Michel Thiebaut |
| author_facet | Foulon, Chris Ovando-Tellez, Marcela Talozzi, Lia Corbetta, Maurizio Matsulevits, Anna de Schotten, Michel Thiebaut |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14309 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emerging-properties Mapping Using Spatial Embedding Statistics: EMUSES Foulon, Chris Ovando-Tellez, Marcela Talozzi, Lia Corbetta, Maurizio Matsulevits, Anna de Schotten, Michel Thiebaut Emerging Technologies Machine Learning 68T10, 62H30, 62P10, 68T09, 92C55 I.5.4; J.3; H.2.8; I.2.6; I.5.1 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. |
| title | Emerging-properties Mapping Using Spatial Embedding Statistics: EMUSES |
| topic | Emerging Technologies Machine Learning 68T10, 62H30, 62P10, 68T09, 92C55 I.5.4; J.3; H.2.8; I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2406.14309 |