4 papers · 1 filter
Explainable Multimodal Regression via Information Decomposition
Zhaozhao Ma, Shujian Yu
Multimodal regression aims to predict a continuous target from heterogeneous input sources and typically relies on fusion strategies such as early or late fusion. However, existing…
Multimodal Functional Maximum Correlation for Emotion Recognition
Deyang Zheng, Tianyi Zhang, Wenming Zheng +1
Emotional states manifest as coordinated yet heterogeneous physiological responses across central and autonomic systems, posing a fundamental challenge for multimodal representatio…
InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis
Shiqin Tang, Shujian Yu
Extracting meaningful latent representations from high-dimensional sequential data is a crucial challenge in machine learning, with applications spanning natural science and engine…
Deep Dynamic Probabilistic Canonical Correlation Analysis
Shiqin Tang, Shujian Yu, Yining Dong +1
This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D2PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynam…