不同分布的多个数据序列分析：用遍历序列生成和多重重加权合成确定公共主成分轴
Analysis of multiple data sequences with different distributions: defining common principal component axes by ergodic sequence generation and multiple reweighting composition
摘要：
主成分分析（PCA）定义了一个由PC轴描述的多维数据序列的减少空间，以捕捉数据的变化。在实践中，我们需要多个数据序列，精确地服从各个概率分布，并且为了对序列进行公平比较，我们需要PC轴，该PC轴对于多个序列是共同的，但适当地捕获这些多个分布。对于这些要求，我们提出了个人遍历采样这些序列，并提供特殊的重新加权恢复目标分布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
