Abstract
With the advancements in smartphones and inbuilt sensors, the day-to-day spatiotemporal activities of people can be recorded. With this available information, the automated extraction of spatiotemporal patterns is crucial to understand the people’s mobility. These patterns can assist in improving the smart city environments like traffic control, urban planning, and transportation facilities. The smartphone generated spatiotemporal data is enriched with multiple contexts and efficiently utilizing them in a Machine Learning process is still a challenging task. In this paper, we propose a Nonnegative Coupled Matrix Tensor Factorization (CMTF) model to integrate and analyze additional contexts with spatiotemporal data to generate meaningful patterns. We also propose an efficient factorization algorithm based on variable selection to solve the Nonnegative CMTF model that yields accurate spatiotemporal patterns. Our empirical analysis highlights the efficiency of the proposed CMTF model in terms of accuracy and factor goodness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chourabi, H., et al.: Understanding smart cities: an integrative framework. In: 2012 45th Hawaii International Conference on System Science (HICSS). IEEE (2012)
Harrison, C., et al.: Foundations for smarter cities. IBM J. Res. Dev. 54(4), 1–16 (2010)
Hancke, G.P., Hancke Jr., G.P.: The role of advanced sensing in smart cities. Sensors 13(1), 393–425 (2012)
Lau, B.P.L., et al.: Extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data. In: PerCom Workshops. IEEE (2017)
Kefalas, P., Symeonidis, P., Manolopoulos, Y.: A graph-based taxonomy of recommendation algorithms and systems in LBSNs. IEEE Trans. Knowl. Data Eng. 28(3), 604–622 (2016)
Sun, L., Axhausen, K.W.: Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transp. Res. Part B: Methodol. 91, 511–524 (2016)
Kimura, T., et al.: Spatio-temporal factorization of log data for understanding network events. In: 2014 Proceedings IEEE on INFOCOM. IEEE (2014)
Fan, Z., Song, X., Shibasaki, R.: CitySpectrum: a non-negative tensor factorization approach. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2014)
Zheng, Y., et al.: Diagnosing New York city’s noises with ubiquitous data. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2014)
Cai, D., et al.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548–1560 (2011)
Nion, D., Sidiropoulos, N.D.: Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar. IEEE Trans. Sig. Process. 58(11), 5693–5705 (2010)
Symeonidis, P.: Matrix and tensor factorization with recommender system applications. Graph-Based Soc. Media Anal. 39, 187 (2016)
Acar, E., Bro, R., Smilde, A.K.: Data fusion in metabolomics using coupled matrix and tensor factorizations. Proc. IEEE 103(9), 1602–1620 (2015)
Bhargava, P., et al.: Who, what, when, and where: multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In: 24th International Conference on World Wide Web. ACM (2015)
Frolov, E., Oseledets, I.: Tensor methods and recommender systems. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 7(3), e1201 (2017)
Acar, E., Kolda, T.G., Dunlavy, D.M.: All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:1105.3422 (2011)
Hsieh, C.-J., Dhillon, I.S.: Fast coordinate descent methods with variable selection for non-negative matrix factorization. In: KDD. ACM (2011)
Acar, E., et al.: Coupled matrix factorization with sparse factors to identify potential biomarkers in metabolomics. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW). IEEE (2012)
Kiers, H.A.: Towards a standardized notation and terminology in multiway analysis. J. Chemom. 14(3), 105–122 (2000)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Cichocki, A., Anh-Huy, P.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 92(3), 708–721 (2009)
Yang, D., et al.: Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2013)
Papalexakis, E.E., et al.: Turbo-smt: accelerating coupled sparse matrix-tensor factorizations by 200x. In: Proceedings of the 2014 SIAM ICDM. SIAM (2014)
Kimura, K., Kudo, M.: Variable selection for efficient nonnegative tensor factorization. In: ICDM. IEEE (2015)
Han, Y., Moutarde, F.: Analysis of large-scale traffic dynamics in an urban transportation network using non-negative tensor factorization. Int. J. Intell. Transp. Syst. Res. 14(1), 36–49 (2016)
Afshar, A., et al.: CP-ORTHO: an orthogonal tensor factorization framework for spatio-temporal data. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, Redondo Beach (2017)
Acknowledgements
This work is supported by SUTD-MIT International Design Center and NSFC 61750110529.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Balasubramaniam, T., Nayak, R., Yuen, C. (2019). Nonnegative Coupled Matrix Tensor Factorization for Smart City Spatiotemporal Pattern Mining. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-13709-0_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13708-3
Online ISBN: 978-3-030-13709-0
eBook Packages: Computer ScienceComputer Science (R0)