Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes

A continuous-time tensor factorization method is developed for event sequences containing multiple "modalities. " Each data element is a point in a tensor, whose dimensions are associated with the discrete alphabet of the modalities. Each tensor data element has an associated time of occurence and a feature vector. We model such data based on pairwise interactive point processes, and the proposed framework connects pairwise tensor factorization with a feature-embedded point process. The model accounts for interactions within each modality, interactions across different modalities, and continuous-time dynamics of the interactions. Model learning is formulated as a convex optimization problem, based on online alternating direction method of multipliers. Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.

Authors

• Hongteng Xu Renmin University of China
• Dixin Luo School of Computer Science and Technology, Beijing Institute of Technology
• Lawrence Carin

Keywords

• Machine Learning: Data Mining
• Machine Learning: Machine Learning
• Machine Learning: Time-series; Data Streams