Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition
Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts in the data. Many real-world applications are dynamic in nature and so are their data. To deal with this dynamic nature of data, there exist a variety of online tensor decomposition algorithms. A central assumption in all those algorithms is that the number of latent concepts remains fixed throughout the entire stream. However, this need not be the case. Every incoming batch in the stream may have a different number of latent concepts, and the difference in latent concepts from one tensor batch to another can provide insights into how our findings in a particular application behave and deviate over time. In this paper, we define “concept” and “concept drift” in the context of streaming tensor decomposition, as the manifestation of the variability of latent concepts throughout the stream. Furthermore, we introduce SeekAndDestroy (The method name is after (and a tribute to) Metallica’s song from their first album (who also owns the copyright for the name)), an algorithm that detects concept drift in streaming tensor decomposition and is able to produce results robust to that drift. To the best of our knowledge, this is the first work that investigates concept drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy on synthetic datasets, which exhibit a wide variety of realistic drift. Our experiments demonstrate the effectiveness of SeekAndDestroy, both in the detection of concept drift and in the alleviation of its effects, producing results with similar quality to decomposing the entire tensor in one shot. Additionally, in real datasets, SeekAndDestroy outperforms other streaming baselines, while discovering novel useful components. Code related to this paper is available at: https://github.com/ravdeep003/conceptDrift.
KeywordsTensor analysis Streaming Concept drift Unsupervised learning
Research was supported by the Department of the Navy, Naval Engineering Education Consortium under award no. N00174-17-1-0005, the National Science Foundation EAGER Grant no. 1746031, and by an Adobe Data Science Research Faculty Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties.
- 1.Bader, B., Harshman, R., Kolda, T.: Analysis of latent relationships in semantic graphs using DEDICOM. In: Workshop for Algorithms on Modern Massive Data Sets (2006)Google Scholar
- 2.Bader, B.W., Kolda, T.G., et al.: MATLAB Tensor Toolbox Version 2.6, February 2015. http://www.sandia.gov/~tgkolda/TensorToolbox/
- 3.Bifet, A., Gama, J., Pechenizkiy, M., Zliobaite, I.: Handling concept drift: importance, challenges and solutions. PAKDD-2011 Tutorial, Shenzhen, China (2011)Google Scholar
- 5.Papalexakis, E.E., Faloutsos, C., Sidiropoulos, N.D.: ParCube: sparse parallelizable tensor decompositions. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS, vol. 7523, pp. 521–536. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33460-3_39CrossRefGoogle Scholar
- 6.Gujral, E., Pasricha, R., Papalexakis, E.E.: SamBaTen: sampling-based batch incremental tensor decomposition. arXiv preprint arXiv:1709.00668 (2017)
- 7.Harshman, R.: Foundations of the PARAFAC procedure: models and conditions for an “explanatory” multimodal factor analysis (1970)Google Scholar
- 12.Papalexakis, E.E.: Automatic unsupervised tensor mining with quality assessment. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 711–719. SIAM (2016)Google Scholar
- 13.Papalexakis, E.E., Faloutsos, C., Sidiropoulos, N.D.: Tensors for data mining and data fusion: models, applications, and scalable algorithms. ACM Trans. Intell. Syst. Technol. (TIST) 8(2), 16 (2017)Google Scholar
- 15.Webb, G.I., Lee, L.K., Petitjean, F., Goethals, B.: Understanding concept drift. CoRR abs/1704.00362 (2017). http://arxiv.org/abs/1704.00362
- 16.Zhou, S., Vinh, N.X., Bailey, J., Jia, Y., Davidson, I.: Accelerating online CP decompositions for higher order tensors. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1375–1384. ACM (2016)Google Scholar