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Found 4 papers, 1 papers with code
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Low-rank data approximation methods such as matrix (e. g., non-negative matrix factorization) and tensor decompositions (e. g., CANDECOMP/PARAFAC) have demonstrated that they can provide such transparent and interpretable insights.
Logistic principal component analysis via non-convex singular value thresholding
Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relationships inside a multivariate binary data set by exploiting the underlying low rank structure.
Separating common (global and local) and distinct variation in multiple mixed types data sets
First, the separation of information that is common across all or some of the data sets, and the information that is specific to each data set is problematic.
Generalized Simultaneous Component Analysis of Binary and Quantitative data
To this end, we propose the generalized SCA (GSCA) model, which takes into account the distinct mathematical properties of binary and quantitative measurements in the maximum likelihood framework.
Methodology
