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Found 3 papers, 3 papers with code
Fast Rank-1 NMF for Missing Data with KL Divergence
We propose a fast non-gradient-based method of rank-1 non-negative matrix factorization (NMF) for missing data, called A1GM, that minimizes the KL divergence from an input matrix to the reconstructed rank-1 matrix.
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
We present an efficient low-rank approximation algorithm for non-negative tensors.
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix.
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