论文标题
在球体上数据的关节反卷积和无监督的源分离
Joint deconvolution and unsupervised source separation for data on the sphere
论文作者
论文摘要
与其他反问题（例如反卷积）共同解决无监督的源分离是多波长数据分析的核心。当应用于在球体上采样的大型数据时，例如天体物理学中的广阔视野观测，其分析需要设计专用稳健且有效的算法。因此，我们研究了一种新的关节反向卷积/稀疏盲源分离方法，该方法专门用于在球体上采样的数据，即创建的SDECGMCA。它基于预测的最小二乘最小化方案，其准确性被证明是在当前关节反卷积/盲源分离设置中强烈依赖某些正则化方案。为此，引入了一种正则化策略，该策略允许设计一种新的鲁棒和有效算法，这是分析大型球形数据的关键。在玩具示例和现实的天文数据上进行数值实验。
Tackling unsupervised source separation jointly with an additional inverse problem such as deconvolution is central for the analysis of multi-wavelength data. This becomes highly challenging when applied to large data sampled on the sphere such as those provided by wide-field observations in astrophysics, whose analysis requires the design of dedicated robust and yet effective algorithms. We therefore investigate a new joint deconvolution/sparse blind source separation method dedicated for data sampled on the sphere, coined SDecGMCA. It is based on a projected alternate least-squares minimization scheme, whose accuracy is proved to strongly rely on some regularization scheme in the present joint deconvolution/blind source separation setting. To this end, a regularization strategy is introduced that allows designing a new robust and effective algorithm, which is key to analyze large spherical data. Numerical experiments are carried out on toy examples and realistic astronomical data.
