A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)

被引：0
|
作者：
He, Baokun [1 ]
Wan, Guihong [1 ]
Schweitzer, Haim [1 ]
机构：
[1] Univ Texas Dallas, 800 W Campbell Rd, Richardson, TX 75083 USA
来源：
THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2020年 / 34卷
关键词：
PCA;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a "bias trick" that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.
引用
收藏
页码：13807 / 13808
页数：2
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