论文标题
MML概率主成分分析
MML Probabilistic Principal Component Analysis
论文作者
论文摘要
主成分分析（PCA）可能是降低数据维度的最广泛方法。 PCA数据分解中的一个关键问题是决定保留多少个因素。本手稿描述了一种新方法，可以根据贝叶斯的最小消息长度方法自动选择主组件的数量。我们还得出了各向同性残留方差的新估计值，并通过数值实验证明它可以改善通常的最大似然方法。
Principal component analysis (PCA) is perhaps the most widely method for data dimensionality reduction. A key question in PCA decomposition of data is deciding how many factors to retain. This manuscript describes a new approach to automatically selecting the number of principal components based on the Bayesian minimum message length method of inductive inference. We also derive a new estimate of the isotropic residual variance and demonstrate, via numerical experiments, that it improves on the usual maximum likelihood approach.
