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Found 7 papers, 3 papers with code
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence
Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships.
RETHINKING LOCAL LOW RANK MATRIX DETECTION:A MULTIPLE-FILTER BASED NEURAL NETWORK FRAMEWORK
In this work, we first organize the general MLLRR problem into three subproblems based on different low rank properties , and we argue that most of existing efforts focus on only one category, which leaves the other two unsolved.
Geometric All-Way Boolean Tensor Decomposition
Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains.
Denoising individual bias for a fairer binary submatrix detection
Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications.
Supervised clustering of high dimensional data using regularized mixture modeling
Identifying relationships between molecular variations and their clinical presentations has been challenged by the heterogeneous causes of a disease.
Component-wise Adaptive Trimming For Robust Mixture Regression
Existing robust mixture regression methods suffer from outliers as they either conduct parameter estimation in the presence of outliers, or rely on prior knowledge of the level of outlier contamination.
Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation
Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples.
