论文标题
非高斯组件分析：测试信号子空间的尺寸
Non-Gaussian component analysis: testing the dimension of the signal subspace
论文作者
论文摘要
缩小维度是多元数据分析中的常见策略，该策略寻求一个子空间，其中包含后续分析所需的所有有趣功能。非高斯组件分析试图为此目的将数据分为非高斯部分，信号和高斯部分，即噪声。我们将证明可以同时使用两个散点功能来实现此目的，并建议进行自举测试以测试非高斯子空间的维度。然后可以使用测试的顺序应用来估计信号维度。
Dimension reduction is a common strategy in multivariate data analysis which seeks a subspace which contains all interesting features needed for the subsequent analysis. Non-Gaussian component analysis attempts for this purpose to divide the data into a non-Gaussian part, the signal, and a Gaussian part, the noise. We will show that the simultaneous use of two scatter functionals can be used for this purpose and suggest a bootstrap test to test the dimension of the non-Gaussian subspace. Sequential application of the test can then for example be used to estimate the signal dimension.
