17 citations · 47 across the 14 of their papers we have counts for
14 papers
On the efficiency-loss free ordering-robustness of product-PCA
Hung Hung, Su-Yun Huang
This article studies the robustness of the eigenvalue ordering, an important issue when estimating the leading eigen-subspace by principal component analysis (PCA). In Yata and Aos…
Robust self-tuning semiparametric PCA for contaminated elliptical distribution
Hung Hung, Su-Yun Huang, Shinto Eguchi
Principal component analysis (PCA) is one of the most popular dimension reduction methods. The usual PCA is known to be sensitive to the presence of outliers, and thus many robust…
A generalized information criterion for high-dimensional PCA rank selection
Hung Hung, Su-Yun Huang, Ching-Kang Ing
Principal component analysis (PCA) is the most commonly used statistical procedure for dimension reduction. An important issue for applying PCA is to determine the rank, which is t…
A Consistency Theorem for Randomized Singular Value Decomposition
Ting-Li Chen, Su-Yun Huang, Weichung Wang
The singular value decomposition (SVD) and the principal component analysis are fundamental tools and probably the most popular methods for data dimension reduction. The rapid grow…
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy
Szu-Chi Chung, Shao-Hsuan Wang, Po-Yao Niu +3
Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization…
Random Partitioning and Distribution-based Thresholding for Iterative Variable Screening in High Dimensions
Yu-Hsiang Cheng, Tzee-Ming Huang, Su-Yun Huang
In big data analysis, a simple task such as linear regression can become very challenging as the variable dimension grows. As a result, variable screening is inevitable in many…