CSTRM: Contrastive Self-Supervised Trajectory Representation Model for trajectory similarity computation

2022 ◽  
Author(s):  
Xiang Liu ◽  
Xiaoying Tan ◽  
Yuchun Guo ◽  
Yishuai Chen ◽  
Zhe Zhang
Keyword(s):  
Representation Model ◽  
Trajectory Representation ◽  
Trajectory Similarity ◽  
Similarity Computation

Staff Similarity Computation in Technology Innovation Team

2011 ◽  
Vol 204-210 ◽  
pp. 1771-1774 ◽  
Author(s):  
Wei Sun ◽  
Yang Yu
Keyword(s):  
Knowledge Representation ◽  
Technology Innovation ◽  
New Methods ◽  
Representation Model ◽  
Network Feature ◽  
Similarity Algorithm ◽  
Similarity Computation ◽  
Innovation Team

The main objective of this investigation is to explore new similarity algorithms of staff similarity in technology innovation team. First, this paper proposes the knowledge representation model of technology staff based on network, and the cliques after clustering according to network feature expresses the sub-fields. Second, from the view of knowledge contained in technology staff, this paper proposes the similarity algorithm based on VSM and the similarity algorithm based on sub-field. Finally, we use the staff classification of one technology innovation team as case study. The experiment results reveal that the similarity of the new methods is accurate than that of the old method, and the information obtained by the new methods is more than that obtained by the old method.

scholarly journals An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation

2021 ◽  
Vol 225 ◽  
pp. 108803
Author(s):  
Maohan Liang ◽  
Ryan Wen Liu ◽  
Shichen Li ◽  
Zhe Xiao ◽  
Xin Liu ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Learning Method ◽  
Trajectory Similarity ◽  
Similarity Computation

scholarly journals Trajectory Similarity Learning with Auxiliary Supervision and Optimal Matching

2020 ◽  
Author(s):  
Hanyuan Zhang ◽  
Xinyu Zhang ◽  
Qize Jiang ◽  
Baihua Zheng ◽  
Zhenbang Sun ◽  
...  
Keyword(s):  
Real World ◽  
Representation Learning ◽  
Similarity Learning ◽  
Trajectory Data ◽  
Optimal Matching ◽  
Training Samples ◽  
Trajectory Similarity ◽  
Similarity Computation ◽  
Real World Datasets ◽  
Relationship Of

Trajectory similarity computation is a core problem in the field of trajectory data queries. However, the high time complexity of calculating the trajectory similarity has always been a bottleneck in real-world applications. Learning-based methods can map trajectories into a uniform embedding space to calculate the similarity of two trajectories with embeddings in constant time. In this paper, we propose a novel trajectory representation learning framework Traj2SimVec that performs scalable and robust trajectory similarity computation. We use a simple and fast trajectory simplification and indexing approach to obtain triplet training samples efficiently. We make the framework more robust via taking full use of the sub-trajectory similarity information as auxiliary supervision. Furthermore, the framework supports the point matching query by modeling the optimal matching relationship of trajectory points under different distance metrics. The comprehensive experiments on real-world datasets demonstrate that our model substantially outperforms all existing approaches.

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