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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.04609 |
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| _version_ | 1866913780350320640 |
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| author | Kacprzak, Tomasz Kamper, Francois Heiss, Michael W. Janka, Gianluca Dillner, Ann M. Takahama, Satoshi |
| author_facet | Kacprzak, Tomasz Kamper, Francois Heiss, Michael W. Janka, Gianluca Dillner, Ann M. Takahama, Satoshi |
| contents | Recently, linear regression models incorporating an optimal transport (OT) loss have been explored for applications such as supervised unmixing of spectra, music transcription, and mass spectrometry. However, these task-specific approaches often do not generalize readily to a broader class of linear models. In this work, we propose a novel algorithmic framework for solving a general class of non-negative linear regression models with an entropy-regularized OT datafit term, based on Sinkhorn-like scaling iterations. Our framework accommodates convex penalty functions on the weights (e.g. squared-$\ell_2$ and $\ell_1$ norms), and admits additional convex loss terms between the transported marginal and target distribution (e.g. squared error or total variation). We derive simple multiplicative updates for common penalty and datafit terms. This method is suitable for large-scale problems due to its simplicity of implementation and straightforward parallelization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Approximate Algorithms for Optimal Transport Linear Models Kacprzak, Tomasz Kamper, Francois Heiss, Michael W. Janka, Gianluca Dillner, Ann M. Takahama, Satoshi Machine Learning Optimization and Control Recently, linear regression models incorporating an optimal transport (OT) loss have been explored for applications such as supervised unmixing of spectra, music transcription, and mass spectrometry. However, these task-specific approaches often do not generalize readily to a broader class of linear models. In this work, we propose a novel algorithmic framework for solving a general class of non-negative linear regression models with an entropy-regularized OT datafit term, based on Sinkhorn-like scaling iterations. Our framework accommodates convex penalty functions on the weights (e.g. squared-$\ell_2$ and $\ell_1$ norms), and admits additional convex loss terms between the transported marginal and target distribution (e.g. squared error or total variation). We derive simple multiplicative updates for common penalty and datafit terms. This method is suitable for large-scale problems due to its simplicity of implementation and straightforward parallelization. |
| title | Scalable Approximate Algorithms for Optimal Transport Linear Models |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2504.04609 |