1 citations · 1 across the 5 of their papers we have counts for
5 papers
The -PCA Framework: A Unified and Efficiency-Preserving Approach with Robust Variants
Hung Hung, Zhi-Yu Jou, Su-Yun Huang +1
Principal component analysis (PCA) is a fundamental tool in multivariate statistics, yet its sensitivity to outliers and limitations in distributed environments restrict its effect…
A Generalized Mean Approach for Distributed-PCA
Zhi-Yu Jou, Su-Yun Huang, Hung Hung +1
Principal component analysis (PCA) is a widely used technique for dimension reduction. As datasets continue to grow in size, distributed-PCA (DPCA) has become an active research ar…
Information Criterion-Based Rank Estimation Methods for Factor Analysis: A Unified Selection Consistency Theorem and Numerical Comparison
Toshinari Morimoto, Hung Hung, Su-Yun Huang
Over the years, numerous rank estimators for factor models have been proposed in the literature. This article focuses on information criterion-based rank estimators and investigate…
On the asymptotic properties of product-PCA under the high-dimensional setting
Hung Hung, Chi-Chun Yeh, Su-Yun Huang
Principal component analysis (PCA) is a widely used dimension reduction method, but its performance is known to be non-robust to outliers. Recently, product-PCA (PPCA) has been sho…
Sufficient Dimension Reduction via Random-Partitions for Large-p-Small-n Problem
Hung Hung, Su-Yun Huang
Sufficient dimension reduction (SDR) is continuing an active research field nowadays for high dimensional data. It aims to estimate the central subspace (CS) without making distrib…