.png){kind=icon}
返回
Robust algorithms for principal component analysis
.png){kind=icon}
OA
AI
DOI：10.1016/S0167-8655(99)00060-4.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we address the issues related to the design of fuzzy robust principal component analysis (FRPCA) algorithms. The design of robust principal component analysis has been studied in the literature of statistics for over two decades. More recently Xu and Yuille proposed a family of online robust principal component analysis based on statistical physics approach. We extend Xu and Yuille's objective function by using fuzzy membership and derive improved algorithms that can extract the appropriate principal components from the spoiled data set. The difficulty of selecting an appropriate hard threshold in Xu and Yuille's approach is alleviated by replacing the threshold by an automatically selected soft threshold in FRPCA. Artificially generated data sets are used to evaluate the performance of various PCA algorithms. (C) 1999 Elsevier Science B.V. All rights reserved.
Keyword:
principal component analysis
robust algorithm
noise clustering
neural networks
fuzzy theory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
暂无机构信息
引用论文
.png){kind=icon}
Estimating the Directions to Light Sources Using Images of Eye for Reconstructing 3D Human Face
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容