Adaptive Sparse Regression for Block Missing Multimodal Data
AdapDiscom: An Adaptive Sparse Regression Method for High-Dimensional ...
Compute X'X Matrix
Compute X'y Vector
DISCOM: Optimal Sparse Linear Prediction for Block-missing Multi-modal...
Fast AdapDiscom
Fast DISCOM
Generate Covariance Matrix
Get Block Indices
Compute Lambda Max for L1 Regularization using KKT Conditions
Provides adaptive direct sparse regression for high-dimensional multimodal data with heterogeneous missing patterns and measurement errors. 'AdapDISCOM' extends the 'DISCOM' framework with modality-specific adaptive weighting to handle varying data structures and error magnitudes across blocks. The method supports flexible block configurations (any K blocks) and includes robust variants for heavy-tailed distributions ('AdapDISCOM'-Huber) and fast implementations for large-scale applications (Fast-'AdapDISCOM'). Designed for realistic multimodal scenarios where different data sources exhibit distinct missing data patterns and contamination levels. Diakité et al. (2025) <doi:10.48550/arXiv.2508.00120>.
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