摘要
全连接的玻尔兹曼机模型可全面描述稀疏系数间统计依赖关系,但时间复杂度较高.为了提高基于玻尔兹曼机的贝叶斯匹配追踪算法(BM-BMP)的重构速度和质量,本文提出一种改进算法.第一,将BM-BMP算法的最大后验概率(MAP)估计评估值分解为上一次迭代的评估值与增量,使得每次迭代仅需计算增量,极大缩短了计算耗时.第二,利用显著最大后验概率估计值平均的方式,有效近似最小均方误差(MMSE)估计,获得了更小的重构误差.实验结果表明,本文算法比BM-BMP算法的运行时间平均缩短了73.66%,峰值信噪比(PSNR)值平均提高了0.57 d B.
Fully connected Boltzmann machine models can be used to provide a comprehensive description of statistical dependencies between sparse coefficients but with high time complexity. To improve the speed and quality of the Boltzmann machine-Bayesian matching pursuit（ BM-BMP） method,an improved algorithm was proposed. First,the maximum a posteriori（ MAP） estimation of the BM-BMP algorithm is decomposed into its value at the last iteration and an increment; thus,it only needs to calculate the increment in each iteration,which greatly reduces the computational time. Second,by calculating the mean of the significant MAP estimations,an effective approximation is obtained for the minimum mean square error（ MMSE） estimation and a smaller reconstruction error is achieved. Compared with the BM-BMP,this method reduces the running time on average by 73. 66% while improving the peak signal to noise ratio（ PSNR） by 0. 57 d B.
出处
《工程科学学报》
EI
CSCD
北大核心
2017年第8期1254-1260,共7页
Chinese Journal of Engineering
基金
国家自然科学基金资助项目(61327005
61302120)
广东省科技计划资助项目(2017A020214011)
中央高校基本科研业务费资助项目(2017MS039)
关键词
稀疏信号重构
快速贝叶斯匹配追踪
玻尔兹曼机
最小均方误差
sparse signal reconstruction
fast Bayesian matching pursuit
Boltzmann machine
minimum mean square error（MMSE）
