25 citations · 38 across the 4 of their papers we have counts for
4 papers
Surprising Asymptotic Conical Structure in Critical Sample Eigen-Directions
Dan Shen, Haipeng Shen, Hongtu Zhu +1
The aim of this paper is to establish several deep theoretical properties of principal component analysis for multiple-component spike covariance models. Our new results reveal a s…
High Dimensional Principal Component Scores and Data Visualization
Dan Shen, Haipeng Shen, Hongtu Zhu +1
Principal component analysis is a useful dimension reduction and data visualization method. However, in high dimension, low sample size asymptotic contexts, where the sample size i…
A General Framework For Consistency of Principal Component Analysis
Dan Shen, Haipeng Shen, J. S. Marron
A general asymptotic framework is developed for studying consis- tency properties of principal component analysis (PCA). Our frame- work includes several previously studied domains…
Consistency of Sparse PCA in High Dimension, Low Sample Size Contexts
Dan Shen, Haipeng Shen, J. S. Marron
Sparse Principal Component Analysis (PCA) methods are efficient tools to reduce the dimension (or the number of variables) of complex data. Sparse principal components (PCs) are ea…