4 papers
Enhancing Differentially Private Mechanisms via Empirical Bayes
Minwoo Kim, Junyong Park, Sungkyu Jung
Differential privacy (DP) has become the gold standard for ensuring the privacy protection of machine learning and statistical algorithms in recent decades. A plethora of algorithm…
Robust and Differentially Private Principal Component Analysis
Minwoo Kim, Sungkyu Jung
Recent advances have sparked significant interest in the development of privacy-preserving Principal Component Analysis (PCA). However, many existing approaches rely on restrictive…
Adaptive Reference-Guided Estimation of Principal Component Subspace in High Dimensions
Dongsun Yoon, Sungkyu Jung
We propose a novel estimator for the principal component (PC) subspace tailored to the high-dimension, low-sample size (HDLSS) context. The method, termed Adaptive Reference-Guided…
Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness
Sangil Han, Kyoowon Kim, Sungkyu Jung
In this paper, we explore the theoretical properties of subspace recovery using Winsorized Principal Component Analysis (WPCA), utilizing a common data transformation technique tha…