论文标题
基于信息最大化的基于依赖和独立来源的盲源分离方法
An Information Maximization Based Blind Source Separation Approach for Dependent and Independent Sources
论文作者
论文摘要
我们引入了一种新的信息最大化（Infomax）方法，以解决盲源分离问题。所提出的框架为基于决定性最大化的结构化基质分解方法（例如非负和多重矩阵分解）提供了信息理论的观点。为此，我们使用基于协方差的对数确定性的替代关节熵度量，我们称之为对数确定性（LD）熵。两个向量之间的相应（LD）共同信息反映了它们的相关水平。我们将Infomax BSS标准构成，作为在输出向量位于假定的域集中的约束下，分离器的输入和输出之间的LD-杂性信息的最大化。与ICA Infomax方法相反，所提出的信息最大化方法可以分开依赖和独立来源。此外，我们可以在无噪声情况下为完美的分离条件提供有限的样本保证。
We introduce a new information maximization (infomax) approach for the blind source separation problem. The proposed framework provides an information-theoretic perspective for determinant maximization-based structured matrix factorization methods such as nonnegative and polytopic matrix factorization. For this purpose, we use an alternative joint entropy measure based on the log-determinant of covariance, which we refer to as log-determinant (LD) entropy. The corresponding (LD) mutual information between two vectors reflects a level of their correlation. We pose the infomax BSS criterion as the maximization of the LD-mutual information between the input and output of the separator under the constraint that the output vectors lie in a presumed domain set. In contrast to the ICA infomax approach, the proposed information maximization approach can separate both dependent and independent sources. Furthermore, we can provide a finite sample guarantee for the perfect separation condition in the noiseless case.
