期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 11, 页码 2543-2547出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2479645
关键词
Block principal component analysis (BPCA); dimensionality reduction; l(1)-norm; nongreedy strategy; outliers
类别
资金
- National Natural Science Foundation of China [61271123, 61401471, 61571176, 91120302, 61511140099]
- China Postdoctoral Science Foundation [2014M562636]
- Key Research Program of the Chinese Academy of Sciences [KGZD-EW-T03]
Block principal component analysis with l(1)-norm (BPCA-L1) has demonstrated its effectiveness in a lot of visual classification and data mining tasks. However, the greedy strategy for solving the l(1)-norm maximization problem is prone to being struck in local solutions. In this paper, we propose a BPCA with nongreedy l(1)-norm maximization, which obtains better solutions than BPCA-L1 with all the projection directions optimized simultaneously. Other than BPCA-L1, the new algorithm has been evaluated against some popular principal component analysis (PCA) algorithms including PCA-L1 and 2-D PCA-L1 on a variety of benchmark data sets. The results demonstrate the effectiveness of the proposed method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据