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- W2080356920 abstract "Abstract A crucial issue for principal components analysis (PCA) is to determine the number of principal components to capture the variability of usually high-dimensional data. In this article the dimension detection for PCA is formulated as a variable selection problem for regressions. The adaptive LASSO is used for the variable selection in this application. Simulations demonstrate that this approach is more accurate than existing methods in some cases and competitive in some others. The performance of this model is also illustrated using a real example. Keywords: Adaptive LASSODimension reductionECM algorithmProbabilistic PCASingular value decompositionMathematics Subject Classification: 62H2562J07 Acknowledgment The authors thank the editor, associate editor, and anonymous referee for detailed comments that helped to improve the presentation of the article." @default.
- W2080356920 created "2016-06-24" @default.
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- W2080356920 date "2010-05-07" @default.
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- W2080356920 title "Detecting the Dimensionality for Principal Components Model" @default.
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- W2080356920 doi "https://doi.org/10.1080/03610911003778127" @default.
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