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Found 4 papers, 1 papers with code
Fusing heterogeneous data sets
The first one is the type of data, such as metabolomics, proteomics and RNAseq data in genomics.
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
