Cellular Learning: Scattered Data Regression in High Dimensions via Voronoi Cells
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914075362983936 |
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| author | Sastry, Shankar Prasad |
| author_facet | Sastry, Shankar Prasad |
| contents | I present a regression algorithm that provides a continuous, piecewise-smooth function approximating scattered data. It is based on composing and blending linear functions over Voronoi cells, and it scales to high dimensions. The algorithm infers Voronoi cells from seed vertices and constructs a linear function for the input data in and around each cell. As the algorithm does not explicitly compute the Voronoi diagram, it avoids the curse of dimensionality. An accuracy of around 98.2% on the MNIST dataset with 722,200 degrees of freedom (without data augmentation, convolution, or other geometric operators) demonstrates the applicability and scalability of the algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cellular Learning: Scattered Data Regression in High Dimensions via Voronoi Cells Sastry, Shankar Prasad Computational Geometry Machine Learning I present a regression algorithm that provides a continuous, piecewise-smooth function approximating scattered data. It is based on composing and blending linear functions over Voronoi cells, and it scales to high dimensions. The algorithm infers Voronoi cells from seed vertices and constructs a linear function for the input data in and around each cell. As the algorithm does not explicitly compute the Voronoi diagram, it avoids the curse of dimensionality. An accuracy of around 98.2% on the MNIST dataset with 722,200 degrees of freedom (without data augmentation, convolution, or other geometric operators) demonstrates the applicability and scalability of the algorithm. |
| title | Cellular Learning: Scattered Data Regression in High Dimensions via Voronoi Cells |
| topic | Computational Geometry Machine Learning |
| url | https://arxiv.org/abs/2510.03810 |