Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913781025603584 |
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| author | Sun, Xingzhi Liao, Danqi MacDonald, Kincaid Zhang, Yanlei Liu, Chen Huguet, Guillaume Wolf, Guy Adelstein, Ian Rudner, Tim G. J. Krishnaswamy, Smita |
| author_facet | Sun, Xingzhi Liao, Danqi MacDonald, Kincaid Zhang, Yanlei Liu, Chen Huguet, Guillaume Wolf, Guy Adelstein, Ian Rudner, Tim G. J. Krishnaswamy, Smita |
| contents | Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and transporting populations via feasible paths. To address these issues, we introduce Geometry-Aware Generative Autoencoder (GAGA), a novel framework that combines extensible manifold learning with generative modeling. GAGA constructs a neural network embedding space that respects the intrinsic geometries discovered by manifold learning and learns a novel warped Riemannian metric on the data space. This warped metric is derived from both the points on the data manifold and negative samples off the manifold, allowing it to characterize a meaningful geometry across the entire latent space. Using this metric, GAGA can uniformly sample points on the manifold, generate points along geodesics, and interpolate between populations across the learned manifold using geodesic-guided flows. GAGA shows competitive performance in simulated and real-world datasets, including a 30% improvement over the state-of-the-art methods in single-cell population-level trajectory inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12779 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds Sun, Xingzhi Liao, Danqi MacDonald, Kincaid Zhang, Yanlei Liu, Chen Huguet, Guillaume Wolf, Guy Adelstein, Ian Rudner, Tim G. J. Krishnaswamy, Smita Machine Learning Differential Geometry Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and transporting populations via feasible paths. To address these issues, we introduce Geometry-Aware Generative Autoencoder (GAGA), a novel framework that combines extensible manifold learning with generative modeling. GAGA constructs a neural network embedding space that respects the intrinsic geometries discovered by manifold learning and learns a novel warped Riemannian metric on the data space. This warped metric is derived from both the points on the data manifold and negative samples off the manifold, allowing it to characterize a meaningful geometry across the entire latent space. Using this metric, GAGA can uniformly sample points on the manifold, generate points along geodesics, and interpolate between populations across the learned manifold using geodesic-guided flows. GAGA shows competitive performance in simulated and real-world datasets, including a 30% improvement over the state-of-the-art methods in single-cell population-level trajectory inference. |
| title | Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds |
| topic | Machine Learning Differential Geometry |
| url | https://arxiv.org/abs/2410.12779 |