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- W1557929987 abstract "The chapter focuses first on shrinking the eigenvalues, and then an overview of various methods for regularizing the eigenvectors of the sample covariance matrix. Recognizing that principal component analysis (PCA) and the singular value decomposition (SVD) are closely related to each other, the chapter presents regression-based methods for sparse PCA. Some of the details of implementing sparse PCA such as defining the percentage of variance explained by the PCs and choosing the tuning parameter are illustrated through a real data example in another section. Sparse SVD (SSVD) and inconsistency of PCA in high dimensions are also presented. The chapter concludes with a brief discussion on principal subspace estimation." @default.
- W1557929987 created "2016-06-24" @default.
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- W1557929987 date "2013-06-24" @default.
- W1557929987 modified "2023-10-17" @default.
- W1557929987 title "Regularizing the Eigenstructure" @default.
- W1557929987 doi "https://doi.org/10.1002/9781118573617.ch4" @default.
- W1557929987 hasPublicationYear "2013" @default.
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