Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
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发表时间：
2015-11
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通讯作者：
Hiroaki Sasaki;Gang Niu;Masashi Sugiyama
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作者：
Hiroaki Sasaki;Gang Niu;Masashi Sugiyama
非高斯分量分析(NGCA)的目的是识别一个线性子空间，使投影数据服从非高斯分布。本文提出了一种基于对数密度梯度估计的NGCA算法。与已有方法不同，NGCA算法通过特征值分解来确定线性子空间，不需要任何迭代过程，因此在计算上是合理的。此外，通过理论分析，我们证明了所识别的子空间在最优参数速率下收敛到真子空间。最后，在人工数据集和基准数据集上验证了该算法的实际性能。
Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a non-Gaussian distribution. In this paper, we propose a novel NGCA algorithm based on log-density gradient estimation. Unlike existing methods, the proposed NGCA algorithm identifies the linear subspace by using the eigenvalue decomposition without any iterative procedures, and thus is computationally reasonable. Furthermore, through theoretical analysis, we prove that the identified subspace converges to the true subspace at the optimal parametric rate. Finally, the practical performance of the proposed algorithm is demonstrated on both artificial and benchmark datasets.