83 citations · 308 across the 26 of their papers we have counts for
3 papers · 2 filters
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Shaojie Xu, Joel Vaughan, Jie Chen +2
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling. It is an unsupervised learning techniq…
Bias, Fairness, and Accountability with AI and ML Algorithms
Nengfeng Zhou, Zach Zhang, Vijayan N. Nair +3
The advent of AI and ML algorithms has led to opportunities as well as challenges. In this paper, we provide an overview of bias and fairness issues that arise with the use of ML a…
Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model
Agus Sudjianto, Jinwen Qiu, Miaoqi Li +1
A new ensemble framework for interpretable model called Linear Iterative Feature Embedding (LIFE) has been developed to achieve high prediction accuracy, easy interpretation and ef…