Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: From data mining to computer vision, from visual surveillance to biometrics research, from biomedical imaging to bioinformatics, and from multimedia retrieval to information management, a large amount of data are naturally represented by multidimensional arrays, i.e.,  tensors . Hover, conventional probabilistic graphical models with probabilistic inference only model data in  vector format, although they are very important in many statistical problems, e.g., model selection. Is it possible to construct multilinear probabilistic graphical models for  tensor format data to conduct probabilistic inference, e.g., model selection?  provides a positive ansr based on the proposed decoupled probabilistic model by developing the  probabilistic tensor analysis (PTA), which selects suitable model for  tensor format data modeling based on  Akaike information criterion (AIC) and  Bayesian information criterion (BIC). Empirical studies demonstrate that PTA associated with AIC and BIC selects correct number of models.
['Dacheng Tao', 'Jimeng Sun', 'Xindong Wu', 'Xuelong Li', 'Jialie Shen', 'Stephen J. Maybank', 'Christos Faloutsos']
I am writing a research paper on a idea Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria
[{"from": "human", "value": "I am writing a research paper on a idea Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: From data mining to computer vision, from visual surveillance to biometrics research, from biomedical imaging to bioinformatics, and from multimedia retrieval to information management, a large amount of data are naturally represented by multidimensional arrays, i.e.,  tensors . Hover, conventional probabilistic graphical models with probabilistic inference only model data in  vector format, although they are very important in many statistical problems, e.g., model selection. Is it possible to construct multilinear probabilistic graphical models for  tensor format data to conduct probabilistic inference, e.g., model selection?  provides a positive ansr based on the proposed decoupled probabilistic model by developing the  probabilistic tensor analysis (PTA), which selects suitable model for  tensor format data modeling based on  Akaike information criterion (AIC) and  Bayesian information criterion (BIC). Empirical studies demonstrate that PTA associated with AIC and BIC selects correct number of models."}]