Orthogonal component analysis: A fast dimensionality reduction algorithm
Orthogonal component analysis: A fast dimensionality reduction algorithm
复制标题
正交分量分析：一种快速降维算法
DOI：
10.1016/j.neucom.2015.11.012
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发表时间：
2016-02
期刊：
影响因子：
6
通讯作者：
Zhao Jinxi
中科院分区：
文献类型：
--
作者：
Zhu Tao;Xu Ye;Shen Furao;Zhao Jinxi
Most existing dimensionality reduction algorithms have two disadvantages: their computational cost is high and they cannot estimate the intrinsic dimension of the original dataset by themselves. To deal with these problems, in this paper we propose a fast linear dimensionality reduction method named Orthogonal Component Analysis (OCA). While avoiding solving eigenproblem and matrix inverse problem, OCA successfully achieves high-speed orthogonal component extraction. By proposing an adaptive threshold scheme, OCA is able to estimate the dimension of the feature space automatically. Meanwhile, the algorithm is guaranteed to be numerical stable. In the experiments, OCA is compared with several typical dimensionality reduction algorithms. The experimental results demonstrate that as a universal algorithm, OCA is efficient and effective.
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影响因子：
23.6
作者：
YanShuicheng;Xudong;ZhangBenyu;ZhangHong-Jiang;YangQiang;LinStephen
通讯作者：
YanShuicheng;Xudong;ZhangBenyu;ZhangHong-Jiang;YangQiang;LinStephen
DOI：
10.1007/bfb0015522
发表时间：
1996-04
期刊：
--
影响因子：
--
作者：
P. Belhumeur;J. Hespanha;D. Kriegman
通讯作者：
P. Belhumeur;J. Hespanha;D. Kriegman
DOI：
10.1016/s0169-7161(82)02018-5
发表时间：
1982
期刊：
--
影响因子：
--
作者：
K. Fukunaga
通讯作者：
K. Fukunaga
影响因子：
10.6
作者：
Cai, Deng;He, Xiaofei;Zhang, Hong-Jiang
通讯作者：
Zhang, Hong-Jiang
DOI：
--
发表时间：
1996
期刊：
--
影响因子：
--
作者：
C. Merz
通讯作者：
C. Merz