We propose a multimodal data fusion framework to systematically analyze complex human behavioral data from specialized domains that are inherently dynamic, sparse, and heterogeneous. We develop a novel two-tier architecture of probabilistic mixtures, where the lower tier leverages parametric distributions from the exponential family to extract significant behavioral patterns from each data modality. These patterns are then organized into a dynamic latent state space at the higher tier to fuse patterns from different modalities. In addition, our framework jointly performs pattern discovery and maximum-margin learning for downstream classification tasks by using a novel group-wise sparse prior that regularizes the coefficients of the maximum-margin classifier. Therefore, the discovered patterns are highly interpretable and discriminative to support downstream classification tasks. Experiments on real-world behavioral data from medical and psychological domains demonstrate that our framework discovers meaningful multimodal behavioral patterns with outstanding interpretability and prediction performance.

Sparse Maximum Margin Learning From Multimodal Human Behavioral Patterns

CMS: Brain Modeling / HAI: Humans and AI

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Zhi Zheng

1

Presentations

Sparse Maximum Margin Learning From Multimodal Human Behavioral Patterns

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