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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/2508.20618 |
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| _version_ | 1866915468015566848 |
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| author | Mehta, Ronak Otto, Mateus Piovezan Stanis, Noah Yazdan-Shahmorad, Azadeh Harchaoui, Zaid |
| author_facet | Mehta, Ronak Otto, Mateus Piovezan Stanis, Noah Yazdan-Shahmorad, Azadeh Harchaoui, Zaid |
| contents | We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex optimization and the improved interpretability of the independent components. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20618 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation Mehta, Ronak Otto, Mateus Piovezan Stanis, Noah Yazdan-Shahmorad, Azadeh Harchaoui, Zaid Machine Learning We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex optimization and the improved interpretability of the independent components. |
| title | Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.20618 |