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Kernel selection with evolutionary algorithm for multiple kernel independent component analysis

机译：基于进化算法的内核选择，用于多核独立成分分析

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Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model is a more difficult problem if one has not sufficient experience. To deal with this problem, an evolution based method to select the kernel model of KICA is proposed in this paper. There are two main features of the proposed method: one is that using a multiple kernel model, a convex combination of several single kernels, replaces the single kernel model; another is that particle swarm optimization (PSO) algorithm is utilized to find the combination weights of the composite kernel. Experiments conducted on separating one-dimensional mixed signals, nature images, and spectroscopic CCD images showed that using multiple kernels model with PSO kernel selection algorithm can enhance the performance of KICA.
机译：内核独立组件分析（KICA）在盲源分离中具有重要的应用，其中如何选择包括内核功能形式及其参数在内的最佳内核是获得最佳性能的关键问题。在实践中，根据经验，通常选择单个内核作为KICA的内核模型。但是，如果没有足够的经验，那么选择合适的内核模型将是一个更加困难的问题。针对这一问题，提出了一种基于进化的KICA内核模型选择方法。所提出的方法有两个主要特点：一是使用多核模型，即多个单核的凸组合，代替了单核模型。另一个是利用粒子群优化（PSO）算法找到复合核的组合权重。对一维混合信号，自然图像和CCD光谱图像进行分离的实验表明，将多核模型与PSO核选择算法配合使用可以提高KICA的性能。

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