Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
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 more 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. The model extends probabilistic principal components analysis to multiple data sets. Our maximum likelihood approach simultaneously estimates joint and individual components, which can lead t...
| Authors | Raphiel J. Murden, Ganzhong Tian, Deqiang Qiu et al. |
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| Primary category | stat.ML |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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