A covariance-free iterative algorithm for distributed principal component analysis on vertically partitioned data
A covariance-free iterative algorithm for distributed principal component analysis on vertically partitioned data
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DOI：
10.1016/j.patcog.2011.09.002
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发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Yue-Fei Guo;Xiaodong Lin;Zhou Teng;X. Xue;Jianping Fan
中科院分区：
文献类型：
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作者：
Yue-Fei Guo;Xiaodong Lin;Zhou Teng;X. Xue;Jianping Fan
本文提出了一种无协方差迭代算法来实现垂直划分的高维数据集的分布式主成分分析。我们已经证明了我们的迭代算法以指数速度单调收敛。不同于现有的技术，旨在逼近全局PCA，我们的无协方差迭代分布式PCA（CIDPCA）算法可以直接估计主成分，而无需计算样本协方差矩阵。因此，可以实现传输成本的显著降低。此外，与现有的分布式PCA技术相比，CIDPCA可以提供更准确的主成分估计和分类结果。我们已经证明了上级性能的CIDPCA通过多个现实世界的数据集的研究。
In this paper, a covariance-free iterative algorithm is developed to achieve distributed principal component analysis on high-dimensional data sets that are vertically partitioned. We have proved that our iterative algorithm converges monotonously with an exponential rate. Different from existing techniques that aim at approximating the global PCA, our covariance-free iterative distributed PCA (CIDPCA) algorithm can estimate the principal components directly without computing the sample covariance matrix. Therefore a significant reduction on transmission costs can be achieved. Furthermore, in comparison to existing distributed PCA techniques, CIDPCA can provide more accurate estimations of the principal components and classification results. We have demonstrated the superior performance of CIDPCA through the studies of multiple real-world data sets.