Name
Abstract | ||
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This paper presents a factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, an event is often encoded by a single discrete variable (marker). We describe the factorial marked point processes whereby time-stamped event is factored into multiple markers. Accordingly the size of the infectivity matrix modeling the effect between pairwise markers is in exponential order regarding the number of discrete markers. We propose a decoupled learning method with two learning procedures: i) directly solving the model based on two techniques: Alternating Direction Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii) involving a reformulation that transforms the original problem into a Logistic Regression model for more efficient learning. Moreover, a sparse group regularizer is added to identify the key profile features and event labels. Empirical results on real world datasets demonstrate the efficiency of our decoupled and reformulated method.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3219819.3220035 | KDD |
Keywords | DocType | Volume |
Factorial Temporal Point Process,Decoupled Learning,Alternating Direction Method of Multipliers,Fast Iterative Shrinkage-Thresholding Algorithm | Conference | abs/1801.06805 |
ISBN | Citations | PageRank |
978-1-4503-5552-0 | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weichang Wu | 1 | 4 | 2.45 |
| Junchi Yan | 2 | 891 | 83.36 |
| Xiaokang Yang | 3 | 3581 | 238.09 |
| Hongyuan Zha | 4 | 6703 | 422.09 |