PLANC: Parallel Low Rank Approximation with Non-negativity Constraints

30 Aug 2019 Eswar Srinivas Hayashi Koby Ballard Grey Kannan Ramakrishnan Matheson Michael A. Park Haesun

We consider the problem of low-rank approximation of massive dense non-negative tensor data, for example to discover latent patterns in video and imaging applications. As the size of data sets grows, single workstations are hitting bottlenecks in both computation time and available memory... (read more)

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  • NUMERICAL ANALYSIS
  • DISTRIBUTED, PARALLEL, AND CLUSTER COMPUTING
  • MATHEMATICAL SOFTWARE
  • NUMERICAL ANALYSIS