311 citations · 447 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
Adjusting systematic bias in high dimensional principal component scores
Sungkyu Jung
Principal component analysis continues to be a powerful tool in dimension reduction of high dimensional data. We assume a variance-diverging model and use the high-dimension, low-s…
Small sphere distributions for directional data with application to medical imaging
Byungwon Kim, Stephan Huckemann, Jörn Schulz +1
We propose new small-sphere distributional families for modeling multivariate directional data on for and . In a special case of univariat…
Incorporating Covariates into Integrated Factor Analysis of Multi-View Data
Gen Li, Sungkyu Jung
In modern biomedical research, it is ubiquitous to have multiple data sets measured on the same set of samples from different views (i.e., multi-view data). For example, in genetic…
Uniqueness questions in a scaling-rotation geometry on the space of symmetric positive-definite matrices
David Groisser, Sungkyu Jung, Armin Schwartzman
Jung et al. (2015) introduced a geometric structure on , the set of symmetric positive-definite matrices, based on eigen-decomposition. Eigenstructure…