Pattern-Calibrated Multimodal Prediction under Blockwise Missingness

Blockwise missingness in multimodal data is usually treated as an incomplete-input problem.

We instead focus on prediction for a prespecified observed-modality pattern, where the observed modality set determines the information on which the prediction rule can condition.

A procedure that imputes missing modalities, zero-fills unobserved modalities, or trains a single pooled predictor may borrow information across patterns, but it can also mix pattern-specific prediction rules.

We propose Multimodal Overlap-aware Shared-specific Alignment and Inter-pattern Calibration (MOSAIC), a pattern-calibrated framework for borrowing across missingness patterns without collapsing their prediction rules.

MOSAIC learns shared and modality-specific representations, uses the available representations that overlap with the target pattern to fit a first-stage predictor, and then estimates the calibration gap from target-pattern data.

We establish non-asymptotic bounds that decompose the error into overlap effective sample size, calibration gap, and representation-learning error, clarifying when cross-pattern borrowing improves over local fitting and when the improvement is controlled by rule mismatch or representation-learning error.

Simulations examine representation recovery and target-pattern correction, and applications to ICU mortality prediction, emotion recognition, and glaucoma classification show gains when target-pattern samples are limited or pattern-specific rules differ.