.png){kind=icon}
返回
Orthogonal component analysis: A fast dimensionality reduction algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.11.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Dimensionality reduction
Orthogonal component
Intrinsic dimension estimation
High-speed component learning
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Avascular Necrosis With Complete Resorption of the Proximal Humerus Following Undisplaced Three-Part Fracture.png)
Orthopedics
OrthopedicsIF0
PREAI
分享收藏
Global bifurcations and chaos for a rotor-active magnetic bearing system with time-varying stiffness
PREAI
分享收藏A comparative study of dimensionality reduction methods for large-scale image retrievalNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容