Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916790681993216 |
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| author | Koka, Taulant Tsakiris, Manolis C. Haro, Benjamín Béjar Muma, Michael |
| author_facet | Koka, Taulant Tsakiris, Manolis C. Haro, Benjamín Béjar Muma, Michael |
| contents | Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex signal structures and broadens the framework to tasks like compressed sensing. These mismatches between samples and channels often arise in applications such as whole-brain calcium imaging of freely moving organisms or multi-target tracking. We improve over previous models by deriving tighter bounds on the required number of samples for unique reconstruction, while supporting more general signal types. The approach is validated through an application in whole-brain calcium imaging, where organism movements disrupt sample-to-neuron mappings. This demonstrates the utility of our framework in real-world settings with imprecise sample-channel associations, achieving accurate signal reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09773 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces Koka, Taulant Tsakiris, Manolis C. Haro, Benjamín Béjar Muma, Michael Signal Processing Machine Learning Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex signal structures and broadens the framework to tasks like compressed sensing. These mismatches between samples and channels often arise in applications such as whole-brain calcium imaging of freely moving organisms or multi-target tracking. We improve over previous models by deriving tighter bounds on the required number of samples for unique reconstruction, while supporting more general signal types. The approach is validated through an application in whole-brain calcium imaging, where organism movements disrupt sample-to-neuron mappings. This demonstrates the utility of our framework in real-world settings with imprecise sample-channel associations, achieving accurate signal reconstruction. |
| title | Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.09773 |