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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources
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Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources

机译：基于投影-追踪的非负源盲分离方法

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摘要

This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the inaccessible source matrix is normalized to be column-sum-to-1. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions, which leads to the recovery of the sources. The proposed method is much faster than Chan's method, which has similar assumptions to ours, due to the usage of optimal projection. It is also more advantageous in separating cross-correlated sources than the independence- and uncorrelation-based methods, as it does not employ any statistical information of the sources. Furthermore, the new method does not require the mixing matrix to be nonnegative. Simulation results demonstrate the superior performance of our method.
机译：本文提出了一种基于投影追踪（PP）的非负源盲分离方法。首先，将可用的观察矩阵映射以构建新的混合模型，其中不可访问的源矩阵被标准化为column-sum-to-1。然后，提出了PP方法来解决这个新模型，其中通过在指定方向上将投影跟踪到映射的观测值逐列估计混合矩阵，从而导致源的恢复。所提出的方法比Chan的方法要快得多，Chan的方法由于使用了最佳投影而与我们的假设相似。与基于独立性和不相关性的方法相比，它在分离互相关源中也更有利，因为它不使用源的任何统计信息。此外，新方法不需要混合矩阵为非负。仿真结果证明了我们方法的优越性能。

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