High Dimensional Semiparametric Scale-Invariant Principal Component Analysis.
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis.
复制标题
高维半参数尺度不变的主成分分析。
DOI：
10.1109/tpami.2014.2307886
复制
发表时间：
2014-10
影响因子：
23.6
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Han F;Liu H
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sparse PCA in three aspects: (i) It is robust to modeling assumptions; (ii) It is robust to outliers and data contamination; (iii) It is scale-invariant and yields more interpretable results. We prove that the COCA estimators obtain fast estimation rates and are feature selection consistent when the dimension is nearly exponentially large relative to the sample size. Careful experiments confirm that COCA outperforms sparse PCA on both synthetic and real-world datasets.
