38 citations · 146 across the 7 of their papers we have counts for
7 papers
A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation
Seokhyun Chung, Young Woong Park, Taesu Cheong
Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is bui…
Optimization for L1-Norm Error Fitting via Data Aggregation
Young Woong Park
We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the…
Subset Selection for Multiple Linear Regression via Optimization
Young Woong Park, Diego Klabjan
Subset selection in multiple linear regression aims to choose a subset of candidate explanatory variables that tradeoff fitting error (explanatory power) and model complexity (numb…
Bayesian Network Learning via Topological Order
Young Woong Park, Diego Klabjan
We propose a mixed integer programming (MIP) model and iterative algorithms based on topological orders to solve optimization problems with acyclic constraints on a directed graph.…
Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis
Young Woong Park, Diego Klabjan
Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data. L1…
Algorithms for Generalized Cluster-wise Linear Regression
Young Woong Park, Yan Jiang, Diego Klabjan +1
Cluster-wise linear regression (CLR), a clustering problem intertwined with regression, is to find clusters of entities such that the overall sum of squared errors from regressions…