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electronic health record record medical information of patients over time however different patients might have different number of clinical visits in addition the gap between two clinical visits might vary so how can we model such a temporal irregular data fortunately irregular tensors provide a viable way we can record the information of each patient as a matrix where columns represent medical features and raw indicate different clinical visits now we represent the information of three patients with three different matrices where their column size is the same but the row numbers might vary among three patients irregular tensor is made by stacking n different matrices perfect two factorization model is an appropriate tool for handling such irregular tensors the latent factors discovered by parafact ii model are interpretable to human and can be used to download stream tasks however the existing perfect 2 models have several major limitations first they are developed based on minimizin
