5 papers
Linear Discriminant Analysis with Gradient Optimization
Cencheng Shen, Yuexiao Dong
Linear discriminant analysis (LDA) is a fundamental classification and dimension reduction method that achieves Bayes optimality under Gaussian mixture, but often struggles in high…
Decision Tree Embedding by Leaf-Means
Cencheng Shen, Yuexiao Dong, Carey E. Priebe
Decision trees and random forest remain highly competitive for classification on medium-sized, standard datasets due to their robustness, minimal preprocessing requirements, and in…
A Graph Sufficiency Perspective for Neural Networks
Cencheng Shen, Yuexiao Dong
This paper analyzes neural networks through graph variables and statistical sufficiency. We interpret neural network layers as graph-based transformations, where neurons act as pai…
Principal Graph Encoder Embedding and Principal Community Detection
Cencheng Shen, Yuexiao Dong, Carey E. Priebe +3
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves ver…
High-Dimensional Independence Testing via Maximum and Average Distance Correlations
Cencheng Shen, Yuexiao Dong
This paper investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dim…