A novel algorithm for independent component analysis with reference and methods for its applications.
A novel algorithm for independent component analysis with reference and methods for its applications.
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独立分量分析新算法的借鉴及应用方法
DOI：
10.1371/journal.pone.0093984
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Mi JX
中科院分区：
文献类型：
--
作者：
Mi JX
提出了一种稳定、快速的带参考独立分量分析(ICA-R)算法。这是一种将可用的参考信号结合到ICA对比度函数中以形成约束ICA(CICA)框架下的增广拉格朗日函数的技术。以前的ICA-R算法是通过一种类牛顿学习方式来求解优化问题来构造的。遗憾的是，ICA-R的收敛速度慢和潜在的不收敛限制了ICA-R的能力。本文首先对原有算法的缺陷进行了研究和探讨，然后提出了一种收敛速度更快的稳定算法。本文的另一个亮点是：第一，引入了参考压缩技术和直接获取参考信息的新方法，以促进ICA-R的应用；第二，提出了一种新的ICA-R方法，与其他经典的ICA方法相比，具有新的优势。最后，在合成数据和真实数据上的实验验证了新算法比以往的ICA-R和其他众所周知的方法都有更好的性能。
This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.
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影响因子：
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