Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations.

ABSTRACT: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only integrate diverse modalities but also leverage their complementary information to improve clustering accuracy and insights, especially when dealing with partial observations with missing data. We propose Generalized Probabilistic Canonical Correlation Analysis (GPCCA), an unsupervised method for the integration and joint dimensionality reduction of multi-modal data. GPCCA addresses key challenges in multi-modal data analysis by handling missing values within the model, enabling the integration of more than two modalities, and identifying informative feature

SUBMITTER: Yang T 

PROVIDER: S-EPMC12533326 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature