Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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,  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.
['Kohei Hayashi', 'Takashi Takenouchi', 'Tomohiro Shibata', 'Yuki Kamiya', 'Daishi Kato', 'Kazuo Kunieda', 'Keiji Yamada', 'Kazushi Ikeda']
I am writing a research paper on a idea Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection
[{"from": "human", "value": "I am writing a research paper on a idea Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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,  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."}]