activity
20192021
most citedSupervised clustering of high dimensional data using regularized mixture modeling

12 citations · 29 across the 6 of their papers we have counts for

collaborators

7 papers

stat.ME2021

Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence

Wennan Chang, Pengtao Dang, Changlin Wan +7

In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial…

cs.SI2021★ 10 cited

Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks

Changlin Wan, Muhan Zhang, Wei Hao +3

Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental probl…

cs.LG2020★ 4 cited

Denoising individual bias for a fairer binary submatrix detection

Changlin Wan, Wennan Chang, Tong Zhao +2

Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications. Existing binary matrix factoriza…

stat.ME2020★ 12 cited

Supervised clustering of high dimensional data using regularized mixture modeling

Wennan Chang, Changlin Wan, Yong Zang +2

Identifying relationships between molecular variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the…

cs.LG2020

Geometric All-Way Boolean Tensor Decomposition

Changlin Wan, Wennan Chang, Tong Zhao +2

Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains. Boolean Tensor Decomposition…

stat.ME2020

Component-wise Adaptive Trimming For Robust Mixture Regression

Wennan Chang, Xinyu Zhou, Yong Zang +2

Parameter estimation of mixture regression model using the expectation maximization (EM) algorithm is highly sensitive to outliers. Here we propose a fast and efficient robust mixt…