Matrix Factorization / Decomposition
7 papers with code • 0 benchmarks • 0 datasets
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Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks.
Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.
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.
In this paper, we tackle two important problems in low-rank learning, which are partial singular value decomposition and numerical rank estimation of huge matrices.
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.