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Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection

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8 Author(s)
Hayashi, K. ; Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Nara, Japan ; Takenouchi, T. ; Shibata, T. ; Kamiya, Y.
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In this paper, we study probabilistic modeling of heterogeneously attributed multi-dimensional arrays. The model can manage the heterogeneity by employing an individual exponential-family distribution for each attribute of the tensor array. These entries are connected by latent variables and are shared information across the different attributes. Because a Bayesian inference for our model is intractable, we cast the EM algorithm approximated by using the Lap lace method and Gaussian process. This approximation enables us to derive a predictive distribution for missing values in a consistent manner. Simulation experiments show that our method outperforms other methods such as PARAFAC and Tucker decomposition in missing-values prediction for cross-national statistics and is also applicable to discover anomalies in heterogeneous office-logging data.

Published in:

Data Mining (ICDM), 2010 IEEE 10th International Conference on

Date of Conference:

13-17 Dec. 2010

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