most citedSufficient Dimension Reduction via Random-Partitions for Large-p-Small-n Problem

1 citations · 1 across the 1 of their papers we have counts for

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3 papers

stat.ME2025

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…

stat.ML2024

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…

stat.ME2017★ 1 cited

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…