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Online continuous-time tensor factorization based on pairwise interactive point processes

Publication ,  Conference
Xu, H; Luo, D; Carin, L
Published in: IJCAI International Joint Conference on Artificial Intelligence
January 1, 2018

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.

Duke Scholars

Published In

IJCAI International Joint Conference on Artificial Intelligence

DOI

ISSN

1045-0823

ISBN

9780999241127

Publication Date

January 1, 2018

Volume

2018-July

Start / End Page

2905 / 2911
 

Citation

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Xu, H., Luo, D., & Carin, L. (2018). Online continuous-time tensor factorization based on pairwise interactive point processes. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2018-July, pp. 2905–2911). https://doi.org/10.24963/ijcai.2018/403
Xu, H., D. Luo, and L. Carin. “Online continuous-time tensor factorization based on pairwise interactive point processes.” In IJCAI International Joint Conference on Artificial Intelligence, 2018-July:2905–11, 2018. https://doi.org/10.24963/ijcai.2018/403.
Xu H, Luo D, Carin L. Online continuous-time tensor factorization based on pairwise interactive point processes. In: IJCAI International Joint Conference on Artificial Intelligence. 2018. p. 2905–11.
Xu, H., et al. “Online continuous-time tensor factorization based on pairwise interactive point processes.” IJCAI International Joint Conference on Artificial Intelligence, vol. 2018-July, 2018, pp. 2905–11. Scopus, doi:10.24963/ijcai.2018/403.
Xu H, Luo D, Carin L. Online continuous-time tensor factorization based on pairwise interactive point processes. IJCAI International Joint Conference on Artificial Intelligence. 2018. p. 2905–2911.

Published In

IJCAI International Joint Conference on Artificial Intelligence

DOI

ISSN

1045-0823

ISBN

9780999241127

Publication Date

January 1, 2018

Volume

2018-July

Start / End Page

2905 / 2911