Bayesian compressive sensing and projection optimization
Bayesian compressive sensing and projection optimization
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DOI：
10.1145/1273496.1273544
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
Shihao Ji;L. Carin
中科院分区：
文献类型：
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作者：
Shihao Ji;L. Carin
本文介绍了一个新的问题，机器学习工具可能会产生影响。所考虑的问题被称为“压缩感测”，其中基于K << N个真实的测量来精确地测量维度N的真实的信号。这是在以下假设下实现的：基础信号在某种基础上具有稀疏表示（例如，小波）。在本文中，我们演示了如何在机器学习中开发的技术，特别是稀疏贝叶斯回归和主动学习，可以利用这个新问题。我们还指出了机器学习社区感兴趣的压缩感知未来的研究方向。
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K << N real measurements. This is achieved under the assumption that the underlying signal has a sparse representation in some basis (e.g., wavelets). In this paper we demonstrate how techniques developed in machine learning, specifically sparse Bayesian regression and active learning, may be leveraged to this new problem. We also point out future research directions in compressive sensing of interest to the machine-learning community.