Name
Title | ||
|---|---|---|
A covariance-free iterative algorithm for distributed principal component analysis on vertically partitioned data |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patcog.2011.09.002 | Pattern Recognition |
Keywords | Field | DocType |
principal component analysis,covariance-free iterative algorithm,partitioned data,covariance-free iterative,principal component,accurate estimation,global pca,pca technique,multiple real-world data set,high-dimensional data set,iterative algorithm converges monotonously | Sparse PCA,Data set,Exponential function,Pattern recognition,Sample mean and sample covariance,Iterative method,Non-linear iterative partial least squares,Artificial intelligence,Mathematics,Principal component analysis,Machine learning,Covariance | Journal |
Volume | Issue | ISSN |
45 | 3 | 0031-3203 |
Citations | PageRank | References |
7 | 0.53 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue-Fei Guo | 1 | 172 | 13.22 |
| Xiaodong Lin | 2 | 63 | 4.32 |
| Zhou Teng | 3 | 12 | 5.02 |
| Xiangyang Xue | 4 | 2466 | 154.25 |
| Jianping Fan | 5 | 2677 | 192.33 |