Abstract
In this paper we give an overview about utilizing Frank Wolfe optimization to find interpretable constrained matrix and tensor factorizations. We will particularly concentrate on imposing stochasticity constraints and show how factors of Archetypal Analysis as well as Decomposition Into Directed Components can be found using Frank Wolfe optimization to respectively decompose bipartite matrices and asymmetric similarity tensors. We will show how the derived algorithms perform by presenting case studies from behavioral profiling in digital games.
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References
Bader, B., Harshman, R., Kolda, T.: Temporal analysis of semantic graphs using ASALSAN. In: Proceedings of IEEE ICDM (2007)
Bauckhage, C., Kersting, K., Hoppe, F., Thurau, C.: Archetypal analysis as an Autoencoder. In: Proceedings of Workshop New Challenges in Neural Computation (2015)
Bauckhage, C.: A neural network implementation of Frank-Wolfe optimization. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 219–226. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68600-4_26
Chew, P.A., Bader, B.W., Rozovskaya, A.: Using DEDICOM for completely unsupervised part-of-speech tagging. In: Proceedings of Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics (2009)
Cutler, A., Breiman, L.: Archetypal analysis. Technometrics 36(4), 338–347 (1994)
Frank, M., Wolfe, P.: An algorithm for quadratic programming. Nav. Res. Logist. Q. 3(1–2), 95–110 (1956)
Harshman, R.: Models for analysis of asymmetrical relationships among N objects or stimuli. In: Proceedings of Joint Meeting of the Psychometric Society and the Society for Mathematical Psychology (1978)
Jaggi, M.: Revisiting Frank-Wolfe: projection-free sparse convex optimization. In: Proceedings of ACM ICML (2013)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Morup, M., Hansen, L.: Archetypal analysis for machine learning and data mining. Neurocomputing 80, 54–63 (2012)
Rattinger, A., Wallner, G., Drachen, A., Pirker, J., Sifa, R.: Integrating and inspecting combined behavioral profiling and social network models in Destiny. In: Wallner, G., Kriglstein, S., Hlavacs, H., Malaka, R., Lugmayr, A., Yang, H.-S. (eds.) ICEC 2016. LNCS, vol. 9926, pp. 77–89. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46100-7_7
Sifa, R., Bauckhage, C.: Online \(k\)-maxoids clustering. In: Proceedings of IEEE DSAA (2017)
Sifa, R., Ojeda, C., Cvejoski, K., Bauckhage, C.: Interpretable matrix factorization with stochasticity constrained nonnegative DEDICOM. In: Proceedings of KDML-LWDA (2017)
Sifa, R., Srikanth, S., Drachen, A., Ojeda, C., Bauckhage, C.: Predicting retention in sandbox games with tensor factorization-based representation learning. In: Proceedings of IEEE CIG (2016)
Sifa, R., Bauckhage, C., Drachen, A.: Archetypal game recommender systems. In: Proceedings of KDML-LWA (2014)
Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. JMLR 10, 623–656 (2009)
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Sifa, R. (2018). An Overview of Frank-Wolfe Optimization for Stochasticity Constrained Interpretable Matrix and Tensor Factorization. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_36
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DOI: https://doi.org/10.1007/978-3-030-01421-6_36
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