106 citations · 108 across the 8 of their papers we have counts for
6 papers · 1 filter
Large Scale High-Dimensional Reduced-Rank Linear Discriminant Analysis
Jocelyn T. Chi
Reduced-rank linear discriminant analysis (RRLDA) is a foundational method of dimension reduction for classification that has been useful in a wide range of applications. The goal…
Linear Discriminant Analysis with the Randomized Kaczmarz Method
Jocelyn T. Chi, Deanna Needell
We present a randomized Kaczmarz method for linear discriminant analysis (rkLDA), an iterative randomized approach to binary-class Gaussian model linear discriminant analysis (LDA)…
Population-Based Hierarchical Non-negative Matrix Factorization for Survey Data
Xiaofu Ding, Xinyu Dong, Olivia McGough +6
Motivated by the problem of identifying potential hierarchical population structure on modern survey data containing a wide range of complex data types, we introduce population-bas…
SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based GxE Tests in Biobank Data
Jocelyn T. Chi, Ilse C. F. Ipsen, Tzu-Hung Hsiao +5
The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and the rich colle…
A User-Friendly Computational Framework for Robust Structured Regression with the L Criterion
Jocelyn T. Chi, Eric C. Chi
We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L criterion. In addition to…
-POD: A Method for -Means Clustering of Missing Data
Jocelyn T. Chi, Eric C. Chi, Richard G. Baraniuk
The -means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, is common in many applications. Mainstream ap…