Unified, provably consistent model-based clustering that jointly selects variables and handles MNAR via a data-driven penalty and explicit missingness–class modeling, validated on transcriptomics.

This paper introduces a new simple but efficient learning mechanism for improving the robust alignment between visual and textual modalities by solving shuffling problems.