Abstract
Despite the capability of modeling multi-dimensional (such as spatio-temporal) data, tensor modeling and factorization methods such as Nonnegative Tensor Factorization (NTF) is in infancy for automatically learning mobility patterns of people. The quality of patterns generated by these methods gets affected by the sparsity of the data. This paper introduces a Sparsity constraint Nonnegative Tensor Factorization (SNTF) method and studies how to effectively generate mobility patterns from the Location Based Social Networks (LBSNs) usage data. The factorization process is optimized using the element selection based factorization algorithm, Greedy Coordinate Descent algorithm. Empirical analysis with real-world datasets shows the significance of SNTF in automatically learning accurate mobility patterns. We empirically show that the sparsity constraint in NTF improves the accuracy of patterns for highly sparse datasets and is able to identify distinctive patterns.
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References
Acar, E., Dunlavy, D.M., Kolda, T.G., Mørup, M.: Scalable tensor factorizations for incomplete data. Chemometr. Intell. Lab. Syst. 106(1), 41–56 (2011)
Afshar, A., et al.: CP-ORTHO: an orthogonal tensor factorization framework for spatio-temporal data. In: SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 67. ACM (2017)
Balasubramaniam, T., Nayak, R., Yuen, C.: Understanding urban spatio-temporal usage patterns using matrix tensor factorization. In: IEEE International Conference on Data Mining Workshops, pp. 1497–1498 (2018)
Balasubramaniam, T., Nayak, R., Yuen, C.: 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, pp. 520–532. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13709-0_44
Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015)
Chourabi, H., et al.: Understanding smart cities: an integrative framework. In: Hawaii International Conference on System Sciences, pp. 2289–2297. IEEE (2012)
Du, L., Li, X., Shen, Y.D.: Robust nonnegative matrix factorization via half-quadratic minimization. In: ICDM, pp. 201–210. IEEE (2012)
Fan, Z., Song, X., Shibasaki, R.: CitySpectrum: a non-negative tensor factorization approach. In: International Joint Conference on Pervasive and Ubiquitous Computing, pp. 213–223. ACM (2014)
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)
Harrison, C., et al.: Foundations for smarter cities. IBM J. Res. Dev. 54(4), 1–16 (2010)
Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5(Nov), 1457–1469 (2004)
Hsieh, C.J., Dhillon, I.S.: Fast coordinate descent methods with variable selection for non-negative matrix factorization. In: SIGKDD, pp. 1064–1072. ACM (2011)
Kimura, T., et al.: Spatio-temporal factorization of log data for understanding network events. In: INFOCOM, pp. 610–618. IEEE (2014)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Kruskal, J.B.: Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics. Linear Algebra Appl. 18(2), 95–138 (1977)
Lau, B.P.L., et al.: Extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data. In: PerCom Workshops, pp. 201–206. IEEE (2017)
Luo, P., Peng, J., Fan, J.: l2, 1 norm and hessian regularized non-negative matrix factorization with discriminability for data representation. Appl. Sci. 7(10), 1013 (2017)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Marakkalage, S.H., et al.: Understanding the lifestyle of older population: mobile crowdsensing approach. IEEE TCSS (2018)
Sun, L., Axhausen, K.W.: Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transp. Res. Part B Methodol. 91, 511–524 (2016)
Takane, Y., Young, F.W., De Leeuw, J.: Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika 42(1), 7–67 (1977)
Zheng, Y., Liu, T., Wang, Y., Zhu, Y., Liu, Y., Chang, E.: Diagnosing New York city’s noises with ubiquitous data. In: UbiComp, pp. 715–725. ACM (2014)
Zou, H., Yuan, M.: The f\(\infty \)-norm support vector machine. Statistica Sinica, 379–398 (2008)
Acknowledgments
This research was partly supported by the Lee Kuan Yew Centre for Innovative Cities under Lee Li Ming Programme in Aging Urbanism.
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Balasubramaniam, T., Nayak, R., Yuen, C. (2019). Sparsity Constraint Nonnegative Tensor Factorization for Mobility Pattern Mining. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_45
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DOI: https://doi.org/10.1007/978-3-030-29911-8_45
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