Abstract
This paper describes algorithms for non-negative factorization of sparse matrices and tensors, which is a popular technology in artificial intelligence in general and in computer linguistics in particular. It is proposed to use the latent Dirichlet distribution to reduce matrices and tensors to block-diagonal form for parallelizing computations and accelerating non-negative factorization of linguistic matrices and tensors of extremely large dimension. The proposed model also allows to supplement models with new data without performing non-negative factorization of the entire very large tensor anew from the very beginning.
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