The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2006) is an attractive approach to obtain sparse loadings in principal component analysis (PCA). S-PCA was motivated by reformulating PCA as a least-squares problem so that a lasso penalty on the loading coefficients can be applied. In this article, we propose new estimates to improve S-PCA in the following two aspects. First, we propose a method of simple adaptive sparse principal component analysis (SAS-PCA), which uses the adaptive lasso penalty (Zou 2006; Wang, Li, and Jiang 2007) instead of the lasso penalty in S-PCA. Second, we replace the least-squares objective function in S-PCA by a general least-squares objective function. This formu...
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction i...
AbstractPrincipal component analysis (PCA) is a widely used tool for data analysis and dimension red...
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduct...
The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2...
10.1198/jcgs.2009.0012Journal of Computational and Graphical Statistics181201-21
The Sparse Principal Component Analysis (Sparse PCA) problem is a variant of the classical PCA probl...
Most of the existing procedures for sparse principal component analysis (PCA) use a penalty function...
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We prop...
Principal component analysis (PCA) is a widespread exploratory data analysis tool. Sparse principal ...
© 2018 Curran Associates Inc.All rights reserved. Sparse Principal Component Analysis (SPCA) and Spa...
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We prop...
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction i...
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We prop...
Sparse Principal Components Analysis (SPCA) aims to find principal components with few non-zero load...
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduct...
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction i...
AbstractPrincipal component analysis (PCA) is a widely used tool for data analysis and dimension red...
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduct...
The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2...
10.1198/jcgs.2009.0012Journal of Computational and Graphical Statistics181201-21
The Sparse Principal Component Analysis (Sparse PCA) problem is a variant of the classical PCA probl...
Most of the existing procedures for sparse principal component analysis (PCA) use a penalty function...
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We prop...
Principal component analysis (PCA) is a widespread exploratory data analysis tool. Sparse principal ...
© 2018 Curran Associates Inc.All rights reserved. Sparse Principal Component Analysis (SPCA) and Spa...
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We prop...
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction i...
In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We prop...
Sparse Principal Components Analysis (SPCA) aims to find principal components with few non-zero load...
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduct...
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction i...
AbstractPrincipal component analysis (PCA) is a widely used tool for data analysis and dimension red...
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduct...