Compressed Sensing: Mathematical Foundations, Implementation, and Advanced Optimization Techniques
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916949280161792 |
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| author | Stevenson, Shane Sabagh, Maryam |
| author_facet | Stevenson, Shane Sabagh, Maryam |
| contents | Compressed sensing is a signal processing technique that allows for the reconstruction of a signal from a small set of measurements. The key idea behind compressed sensing is that many real-world signals are inherently sparse, meaning that they can be efficiently represented in a different space with only a few components compared to their original space representation. In this paper we will explore the mathematical formulation behind compressed sensing, its logic and pathologies, and apply compressed sensing to real world signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compressed Sensing: Mathematical Foundations, Implementation, and Advanced Optimization Techniques Stevenson, Shane Sabagh, Maryam Machine Learning Compressed sensing is a signal processing technique that allows for the reconstruction of a signal from a small set of measurements. The key idea behind compressed sensing is that many real-world signals are inherently sparse, meaning that they can be efficiently represented in a different space with only a few components compared to their original space representation. In this paper we will explore the mathematical formulation behind compressed sensing, its logic and pathologies, and apply compressed sensing to real world signals. |
| title | Compressed Sensing: Mathematical Foundations, Implementation, and Advanced Optimization Techniques |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.11550 |