<ul><li>InfoDPCCA is a dynamic probabilistic Canonical Correlation Analysis (CCA) framework designed for extracting meaningful latent representations from high-dimensional sequential data in machine learning.</li><li>It leverages an information-theoretic objective to extract a shared latent representation capturing the mutual structure between data streams as well as separate latent components specific to each sequence.</li><li>Unlike previous dynamic CCA models, InfoDPCCA ensures the shared latent space encodes only the mutual information between sequences, enhancing interpretability and robustness.</li><li>Experiments on synthetic and medical fMRI data show that InfoDPCCA is proficient in representation learning, with code available at https://github.com/marcusstang/InfoDPCCA.</li></ul>

InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis

Discover more