We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the log-linear model equipped with a hierarchically structured sample space, which has theoretical guarantees to uniquely recover a set of source signals by minimizing the KL divergence from a set of mixed signals. Source signals, received signals, and mixing matrices are realized as different layers in our hierarchical sample space. Our empirical results have demonstrated on images and time series data that our approach is superior to well established techniques and is able to separate signals with complex interactions.

翻译：我们提出了一种新型的盲源分离方法,称为信息几何盲源分离(IGBSS ) 。 我们的配方基于配备了分层结构样板空间的日志线性模型,该模型在理论上保证通过将一组混合信号与一组混合信号的差异最小化而独有地恢复一套源信号。 源信号、接收信号和混合矩阵作为我们等级样板空间的不同层而实现。 我们的经验结果在图像和时间序列数据上表明,我们的方法优于既定技术,并且能够将信号与复杂互动分开。