Fast Principal Component Analysis using Eigenspace Merging
Fast Principal Component Analysis using Eigenspace Merging
复制标题
使用特征空间合并进行快速主成分分析
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
T. Tan
中科院分区：
文献类型：
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作者：
Liang Liu;Yunhong Wang;Qian Wang;T. Tan
本文提出了一种处理大规模高维数据集的主成分分析（PCA）快速算法。一个大的数据集首先被划分成几个小的数据集。然后，将传统的PCA方法应用于每个小数据集，得到多个特征空间模型，其中每个特征空间模型是从一个小数据集计算的。最后，将这些特征空间模型合并为一个包含原始数据集的PCA结果的特征空间模型。在FERET数据集上的实验结果表明，该算法比传统的PCA方法速度快得多，而主成分和重建误差与传统方法几乎相同。
In this paper, we propose a fast algorithm for principal component analysis (PCA) dealing with large high-dimensional data sets. A large data set is firstly divided into several small data sets. Then, the traditional PCA method is applied on each small data set and several eigenspace models are obtained, where each eigenspace model is computed from a small data set. At last, these eigenspace models are merged into one eigenspace model which contains the PCA result of the original data set. Experiments on the FERET data set show that this algorithm is much faster than the traditional PCA method, while the principal components and the reconstruction errors are almost the same as that given by the traditional method.