Principal component analysis (PCA) defines a reduced space described by PC axes for a given multidimensional-data sequence to capture the variations of the data. In practice, we need multiple data sequences that accurately obey individual probability distributions and for a fair comparison of the sequences we need PC axes that are common for the multiple sequences but properly capture these multiple distributions. For these requirements, we present individual ergodic samplings for these sequences and provide special reweighting for recovering the target distributions.

中文翻译：

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分析不同分布的多个数据序列：通过遍历序列生成和多重重加权组合定义公共主成分轴

主成分分析 (PCA) 为给定的多维数据序列定义了由 PC 轴描述的缩减空间，以捕获数据的变化。在实践中，我们需要多个数据序列准确地服从个体概率分布，为了公平地比较序列，我们需要多个序列通用但正确捕获这些多个分布的 PC 轴。对于这些要求，我们为这些序列提供了单独的遍历采样，并为恢复目标分布提供了特殊的重新加权。