Kernel independent component analysis
Kernel independent component analysis
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DOI：
10.1162/153244303768966085
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发表时间：
2003-01-01
影响因子：
6
通讯作者：
Jordan, MI
中科院分区：
文献类型：
--
作者：
Bach, FR;Jordan, MI
提出了一类基于再生核希尔伯特空间中典型相关的对比函数的独立分量分析算法。一方面，我们证明了我们的对比度函数与互信息有关，并且具有理想的数学性质作为统计依赖性的度量。另一方面，基于核方法的最新发展，我们证明了这些标准及其导数可以有效地计算。最小化这些标准导致灵活和强大的伊卡算法。我们说明与模拟涉及各种各样的源分布，表明我们的算法优于许多目前已知的算法。
We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria and their derivatives can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety of source distributions, showing that our algorithms outperform many of the presently known algorithms.