最近我和本源等合作了一篇论文，已经投稿到了IEEE SPL。这篇文章里，又一个新的BSBL算法推导出来。该算法尽管性能上稍微弱于BSBL-EM,BSBL-BO等，但是速度非常快，所以比较适合大规模的问题。

文章的信息如下：

Fast Marginalized Block SBL Algorithm
by Benyuan Liu, Zhilin Zhang, Hongqi Fan, Zaiqi Lu, Qiang Fu

下载链接: http://arxiv.org/abs/1211.4909

摘要：
The performance of sparse signal recovery can be improved if both sparsity and correlation structure of signals can be exploited. One typical correlation structure is intra-block correlation in block sparse signals. To exploit this structure, a framework, called block sparse Bayesian learning (BSBL) framework, has been proposed recently. Algorithms derived from this framework showed promising performance but their speed is not very fast, which limits their applications. This work derives an efficient algorithm from this framework, using a  marginalized likelihood maximization method. Thus it can exploit block sparsity and intra-block correlation of signals. Compared to existing BSBL algorithms, it has close recovery performance to them, but has much faster speed. Therefore, it is more suitable for recovering large scale datasets.






转载本文请联系原作者获取授权，同时请注明本文来自张智林科学网博客。
链接地址：https://wap.sciencenet.cn/blog-765184-636353.html

上一篇：纽约时报上关于Deep Learning的研究进展的报道
下一篇：写毕业论文真是个体力活