LEARNING TO RECOVER SPARSE SIGNALS

被引：0
|
作者：
Zhong, Sichen [1 ]
Zhao, Yue [1 ,2 ]
Chen, Jianshu [3 ]
机构：
[1] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA
[2] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA
[3] Tencent AI Lab, Bellevue, WA USA
来源：
2019 57TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON) | 2019年
关键词：
Compressed Sensing; Reinforcement Learning; Monte Carlo Tree Search; Basis Pursuit; Orthogonal Matching Pursuit; ALGORITHM; GAME; GO;
D O I：
10.1109/allerton.2019.8919947
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In compressed sensing, a primary problem to solve is to reconstruct a high dimensional sparse signal from a small number of observations. In this work, we develop a new sparse signal recovery algorithm using reinforcement learning (RL) and Monte Carlo Tree Search (MCTS). Similarly to OMP, our RL+MCTS algorithm chooses the support of the signal sequentially. The key novelty is that the proposed algorithm learns how to choose the next support as opposed to following a pre-designed rule as in OMP. Empirical results are provided to demonstrate the superior performance of the proposed RL+MCTS algorithm over existing sparse signal recovery algorithms.
引用
收藏
页码：995 / 1000
页数：6
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