Training-set-free two-stage deep learning for spectroscopic data de-noising
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911789028999168 |
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| author | Huang, Dongchen Liu, Junde Qian, Tian Weng, Hongming |
| author_facet | Huang, Dongchen Liu, Junde Qian, Tian Weng, Hongming |
| contents | De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training-set-free two-stage deep learning for spectroscopic data de-noising Huang, Dongchen Liu, Junde Qian, Tian Weng, Hongming Materials Science Machine Learning Data Analysis, Statistics and Probability De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge. |
| title | Training-set-free two-stage deep learning for spectroscopic data de-noising |
| topic | Materials Science Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.18830 |