| タイトル | A Note on Support Recovery of Sparse Signals using Linear Programming |
|---|---|
| 著者 | 堀井 俊佑 |
| 年度 | 2016 |
| 形式 | 国際学会 |
| 分野 | その他 |
| 掲載雑誌名 | Proceedings of International Symposium on Information Theory and Its Applications (ISITA2016) |
| 掲載号・ページ | pp.270--274 |
| 掲載年 | 2016 |
| 掲載月 | 10 |
| アブスト （日本語） |
2016 International Symposium on Information Theory and Its Applications (ISITA2016) 2016年10月30日～11月2日（発表日：1日） Monterey, CA, USA 査読有 DOI：なし |
| アブスト （英語） |
A new theory known as compressed sensing considers the problem to acquire and recover a sparse signal from its linear measurements. In this paper, we propose a new support recovery algorithm from noisy measurements based on the linear programming (LP). LP is widely used to estimate sparse signals, however, we focus on the problem to recover the support of sparse signals rather than the problem to estimate sparse signals themselves. First, we derive an integer linear programming (ILP) formulation for the support recovery problem. Then we obtain the LP based support recovery algorithm by relaxing the ILP. The proposed LP based recovery algorithm has an attracting property that the output of the algorithm is guaranteed to be the maximum a posteiori (MAP) estimate when it is integer valued. We compare the performance of the proposed algorithm to a state-of-the-art algorithm named sparse matching pursuit (SMP) via numerical simulations. |
| 備考 （日本語） |
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| 備考 （英語） |
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| 論文原稿 | |
| 発表資料 |
