BMF: Block matrix approach to factorization of large scale data
作者： Vineet C Nair , Prasad G Bhavana
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摘要: Matrix Factorization (MF) on large scale matrices is computationally as well memory intensive task. Alternative convergence techniques are needed when the size of input matrix higher than available a Central Processing Unit (CPU) and Graphical (GPU). While alternating least squares (ALS) CPU could take forever, loading all required to GPU may not be possible dimensions significantly higher. Hence we introduce novel technique that based considering entire data into block relies factorization at level.
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arxiv.org参考文章(12)
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