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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure
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Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

机译：基于行列式测度的稀疏分量非负盲分离

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摘要

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named $D$-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.
机译：本文解决了非负盲源分离（NBSS）问题，其中源和混合矩阵都是非负的。由于许多现实世界信号都是稀疏的，因此我们通过稀疏分量分析来处理NBSS。首先，引入了基于行列式的稀疏度量，称为$ D $ -measure，以度量信号的时间和空间稀疏性。在此基础上，推导了一种新的NBSS模型，并提出了一种迭代稀疏最大化（ISM）方法来求解该模型。在ISM方法中，可以将NBSS问题转化为针对非混合矩阵的逐行优化，然后使用二次编程（QP）技术来优化每一行。此外，我们分析了所提出的ISM-QP方法的源可识别性和计算复杂性。该新方法在源和混合矩阵上要求相对较弱的条件，具有较高的计算效率，并且易于实现。仿真结果证明了该方法的有效性。

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