Earticle

초록

영어

Human mobility estimation plays a key factor in a lot of promising applications including location-based recommendation systems, urban planning, and disease outbreak control. We study the human mobility estimation problem in the case where recent locations of a person-of-interest are unknown. Since matrix decomposition is used to perform latent semantic analysis of multi-dimensional data, we propose a human location estimation algorithm based on matrix factorization to reconstruct the human movement patterns through the use of information of persons with correlated movements. Specifically, the optimization problem which minimizes the difference between the reconstructed and actual movement data is first formulated. Then, the gradient descent algorithm is applied to adjust parameters which contribute to reconstructed mobility data. The experiment results show that the proposed framework can be used for the prediction of human location and achieves higher predictive accuracy than a baseline model.

목차

Abstract
1. Introduction
2. Preliminaries
2.1 Matrix Decomposition
2.2 Datasets
3. Two-phase Human Location Estimation Framework
3.1 Data Structures in the Estimation Framework
3.2 Matrix Decomposition Algorithm
4. Evaluation Results and Analysis
5. Conclusion
Acknowledgement
References

키워드

저자

• Thi-Nga Dao [ Department of Electrical and Computer Engineering, University of Ulsan, Korea ]
• Seokhoon Yoon [ Department of Electrical and Computer Engineering, University of Ulsan, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380