https://scholars.lib.ntu.edu.tw/handle/123456789/559329
標題: | Online Positron Emission Tomography by Online Portfolio Selection | 作者: | YEN-HUAN LI | 關鍵字: | online portfolio selection; online-to-batch conversion; Positron emission tomography; Soft-Bayes; stochastic optimization | 公開日期: | 2020 | 卷: | 2020-May | 起(迄)頁: | 1110-1114 | 來源出版物: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | 摘要: | The number of measurement outcomes in positron emission tomography (PET) is typically large, rendering signal reconstruction computationally expensive. We propose an online algorithm to address this computational issue. The per-iteration computational complexity of the proposed algorithm is independent of the number of measurement outcomes and linear√ in the signal dimension. The algorithm has a rigorous Oleft( {1/sqrt k } right) convergence rate guarantee, where k denotes the iteration counter. Numerical experiments on synthetic data-sets show that the algorithm can be significantly faster than expectation maximization and stochastic primal-dual hybrid gradient method. The proposed algorithm is based on an equivalent stochastic optimization formulation, the Soft-Bayes algorithm for online portfolio selection, and standard online-to-batch conversion. © 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85089211934&partnerID=40&md5=23de0c61a3a5e7f006dce210c0b52353 https://scholars.lib.ntu.edu.tw/handle/123456789/559329 |
ISSN: | 15206149 | DOI: | 10.1109/ICASSP40776.2020.9053230 | SDG/關鍵字: | Audio signal processing; Gradient methods; Maximum principle; Numerical methods; Optimization; Positrons; Signal reconstruction; Speech communication; Computational issues; Expectation - maximizations; Iteration counters; Numerical experiments; On-line algorithms; Positron emission tomography (PET); Stochastic optimizations; Synthetic datasets; Positron emission tomography |
顯示於: | 資訊工程學系 |
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