2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Adaptive Explainable Neural Networks (AxNNs)
Jie Chen, Joel Vaughan, Vijayan N. Nair +1
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the resul…
Explainable Neural Networks based on Additive Index Models
Joel Vaughan, Agus Sudjianto, Erind Brahimi +2
Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of t…