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Description: Our toolbox currently contains implementations of robust methods for
location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-
LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi-
pal component regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation
(RDA). Only a few of these methods will be highlighted in this paper. The toolbox
also provides many graphical tools to detect and classify the outliers. The use of these
features will be explained and demonstrated through the analysis of some real data
sets.
Platform: |
Size: 294912 |
Author: 王一 |
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Description: 基于KPCA（核主元分析）发对TE模型进行故障诊断和辨识-Based on KPCA (KPCA) TE model made of fault diagnosis and identification
Platform: |
Size: 2754560 |
Author: paul |
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Description: 一种健壮的主成分分析算法，根据文献《ROBPCA: a New Approach to Robust Principal Component Analysis》-《ROBPCA: a New Approach to Robust Principal Component Analysis》
Platform: |
Size: 9216 |
Author: 钱叶魁 |
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Description: 介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。-Our toolbox currently contains implementations of robust
methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal
component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification
(RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify
the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.
Platform: |
Size: 429056 |
Author: 杨李 |
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