4 papers
Geometry-preserving and interpretable dimension reduction for compositional data
Junyoung Park, Cheolwoo Park, Jeongyoun Ahn
High-dimensional compositional data pose unique statistical challenges due to the simplex constraint and excess zeros. While dimension reduction is indispensable for analyzing such…
Optimal differentially private kernel learning with random projection
Bonwoo Lee, Cheolwoo Park, Jeongyoun Ahn
Differential privacy has become a cornerstone in the development of privacy-preserving learning algorithms. This work addresses optimizing differentially private kernel learning wi…
Optimal Test-Data Piling in HDLSS Classification with Covariance Heterogeneity
Taehyun Kim, Jeongyoun Ahn, Sungkyu Jung
This work addresses a longstanding question in high-dimensional linear classification: Is perfect classification achievable in heterogeneous covariance structures? We focus on the…
Variable selection and basis learning for ordinal classification
Minwoo Kim, Sangil Han, Jeongyoun Ahn +1
We propose a method for variable selection and basis learning for high-dimensional classification with ordinal responses. The proposed method extends sparse multiclass linear discr…