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Model Selection for Non-Negative Tensor Factorization with Minimum Description Length
Non-negative tensor factorization (NTF) is a widely used multi-way analysis approach that factorizes a high-order non-negative data tensor into several non-negative factor matrices. In NTF, the non-negative rank has to be predetermined to specify the model and it greatly influences the factorized matrices. However, its value is conventionally determined by specialists' insights or trial and error. This paper proposes a novel rank selection criterion for NTF on the basis of the minimumdoi:10.3390/e21070632 pmid:33267345 fatcat:374cxtwqtjbq3pxqt6cklx3rpa