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ICDM
2010
IEEE

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

9 years 3 months ago
Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection
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 Laplace 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 crossnational statistics and is also applicable to discover anomalies in heterogeneous office-logging data. Keywords-tensor factorization; Bayesian probabilistic model; Gaussian process; data fusion;
Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibat
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICDM
Authors Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, Kazushi Ikeda
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