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首页> 外文会议>Asilomar Conference on Signals, Systems, and Computers >Independent Component Analysis Based on Non-polynomial Approximation of Negentropy: Application to MRS Source Separation
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Independent Component Analysis Based on Non-polynomial Approximation of Negentropy: Application to MRS Source Separation

机译：基于非多项式近似的非多项式近似的独立分量分析：应用于MRS源分离的应用

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In this paper, a new ICA algorithm based on non-polynomial approximation of negentropy that captures both the asymmetry of the sources' PDF and the sub/super-Gaussianity of this latter is proposed. A gradient-ascent iteration with quasi-optimal stepsize is used to maximize the considered cost function. With this quasi-optimal computation in the case of highly non-linear objective function, the main advantages of the proposed algorithm are 1) its robustness to outliers compared to kurtosis-based ICA method especially for situations of small data size, and 2) its ability to capture sources' asymmetric probability density functions which is a property that can't be fulfilled in classic ICA algorithms like FastICA. Numerical results reported in the context of source separation of brain magnetic resonance spectroscopy show the superiority of the proposed algorithm over the FastICA algorithm in terms of both source separation accuracy and the number of iterations required for convergence.
机译：本文提出了一种基于非多项式近似的新的ICA算法，其捕获了源的不对称性的PDF和该后者的子/超高斯度。使用准优化步骤的梯度 - 上升迭代用于最大化考虑的成本函数。利用这种准优化的计算在高度线性的物镜的情况下，所提出的算法的主要优点是1）与基于Kurtosis的ICA方法相比，其对异常值的鲁棒性，特别是对于小数据尺寸的情况，而2）其能够捕获源的不对称概率密度函数，这是在像Fastica这样的经典ICA算法中不能满足的属性。在脑磁共振谱源分离的上下文中报告的数值结果表明了在源分离精度和收敛所需的迭代次数方面的特殊算法的优越性。

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