AI RESEARCH
Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
arXiv CS.LG
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ArXi:2603.12351v1 Announce Type: cross Collecting multiple types of data on the same set of subjects is common in modern scientific applications including, genomics, metabolomics, and neuroimaging. Joint and Individual Variance Explained (JIVE) seeks a low-rank approximation of the joint variation between two or sets of features captured on common subjects and isolates this variation from that unique to eachset of features. We develop an expectation-maximization (EM) algorithm to estimate a probabilistic model for the JIVE framework.