311 citations · 447 across the 20 of their papers we have counts for
22 papers
An association measure for mixed-type variables
Yongjae Kim, Haeun Moon, Sungkyu Jung
Quantifying the association between a real-valued variable and a categorical variable is a fundamental task in data analysis. Existing methods often rely on parametric assumptions…
Joint estimation of high-dimensional spiked covariance matrices via a partially shared subspace
Changwon Yoon, Minwoo Kim, Sungkyu Jung +1
Statistical analysis of high-dimensional data is often hampered by limited sample sizes, yet auxiliary datasets from related sources are often readily available. When two such data…
Averaging symmetric positive-definite matrices on the space of eigen-decompositions
Sungkyu Jung, Brian Rooks, David Groisser +1
We study extensions of Fréchet means for random objects in the space of symmetric positive-definite matrices using the scaling-rotation geometric fram…
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…
Integrative decomposition of multi-source data by identifying partially-joint score subspaces
SeoWon Gabriel Choi, Sungkyu Jung
Analysis of multi-source dataset, where data on the same objects are collected from multiple sources, is of rising importance in many fields, most notably in multi-omics biology. A…