Memory-efficient parallel computation of tensor and matrix products for big tensor decomposition
Pacific Grove, CA(2014)
摘要
Low-rank tensor decomposition has many applications in signal processing and machine learning, and is becoming increasingly important for analyzing big data. A significant challenge is the computation of intermediate products which can be much larger than the final result of the computation, or even the original tensor. We propose a scheme that allows memory-efficient in-place updates of intermediate matrices. Motivated by recent advances in big tensor decomposition from multiple compressed replicas, we also consider the related problem of memory-efficient tensor compression. The resulting algorithms can be parallelized, and can exploit but do not require sparsity.
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关键词
Big Data,data analysis,mathematics computing,matrix decomposition,parallel algorithms,tensors,big data analysis,big tensor decomposition,compressed replicas,low-rank tensor decomposition,machine learning,matrix products,memory-efficient parallel computation,signal processing,tensor products
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