Abstract Details
Activity Number:
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137
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #311622
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Title:
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Robust Analysis of Clustered Principal Component Analysis Method for Large Multivariate Data
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Author(s):
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Nasser Fard and Yuanchen Fang*+ and Huyang Xu
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Companies:
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Northeastern University and Northeastern University and Northeastern University
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Keywords:
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Correlation Matrix ;
Clustering ;
PCA ;
Robust Analysis ;
Weighted Variance ;
Eigen Value
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Abstract:
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Weighted-variances clustering is used for constructing interpretable components from clusters of variables. This will lead to determination of the important variables constituting a certain component are more correlated to each other than to the other variables. Variables are optimally clustered using the objective criterion to obtain the nonzero-loadings of an interpretable component from the correlation matrix of the variables in their corresponding cluster while the loading of the remaining variables become zero. A significant advantage of weighted-variance clustering component analysis is that the optimal number of PCs are determined, which avoids the subjective bias caused by fixing a priori number. A robust correlation matrix, instead of empirical correlation matrix throughout the weighted-variance algorithm is utilized. A robust correlation measure, which is derived from minimum covariance determinant (MCD) estimator, into the weighted-variance clustering model is introduced.
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Authors who are presenting talks have a * after their name.
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