16 citations · 35 across the 9 of their papers we have counts for
9 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…
Reproducing Kernels and New Approaches in Compositional Data Analysis
Binglin Li, Jeongyoun Ahn
Compositional data, such as human gut microbiomes, consist of non-negative variables whose only the relative values to other variables are available. Analyzing compositional data s…
Sparse Functional Linear Discriminant Analysis
Juhyun Park, Jeongyoun Ahn, Yongho Jeon
Functional linear discriminant analysis offers a simple yet efficient method for classification, with the possibility of achieving a perfect classification. Several methods are pro…
Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem
Sungkyu Jung, Jeongyoun Ahn, Yongho Jeon
We propose a new algorithm for sparse estimation of eigenvectors in generalized eigenvalue problems (GEP). The GEP arises in a number of modern data-analytic situations and statist…
On the number of principal components in high dimensions
Sungkyu Jung, Myung Hee Lee, Jeongyoun Ahn
We consider the problem of how many components to retain in the application of principal component analysis when the dimension is much higher than the number of observations. To es…
Sparse Generalized Eigenvalue Problem with Application to Canonical Correlation Analysis for Integrative Analysis of Methylation and Gene Expression Data
Sandra E. Safo, Jeongyoun Ahn, Yongho Jeon +1
We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting h…