A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data

Understand

Connectivity estimation is challenging in the context of high-dimensional data.

• A useful preprocessing step is to group variables into clusters, however, it is not always clear how to do so from the perspective of connectivity estimation.
• Another practical challenge is that we may have data from multiple related classes (e.g., multiple subjects or conditions) and wish to incorporate constraints on the similarities across classes.
• We propose a probabilistic model which simultaneously performs both a grouping of variables (i.e., detecting community structure) and estimation of connectivities between the groups which correspond to latent variables.