17 citations · 47 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
Detection of Gene-Gene Interactions by Multistage Sparse and Low-Rank Regression
Hung Hung, Yu-Tin Lin, Pengwen Chen +3
A daunting challenge faced by modern biological sciences is finding an efficient and computationally feasible approach to deal with the curse of high dimensionality. The problem be…
Robust Independent Component Analysis via Minimum Divergence Estimation
Peng-Wen Chen, Hung Hung, Osamu Komori +2
Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article w…