K. M. Azharul Hasan
Md. Safayet Hossain
ABSTRACT
Large scale high dimensional multi-aspect data are naturally represented as tensors or multi-way arrays for scientific and engineering computations. But in practical applications such as signal processing, data mining, computer vision, and graph analysis, this tensor data is very sparse. The degree of sparsity is magnified by the increase of number of dimensions for large tensors. Therefore, storing and applying tensor operations on this highly sparse multidimensional data is an important research challenge for data scientists. In this paper, we describe a new sparse tensor storage format that provide storage benefits and are independent to the number of dimension of the tensor. Efficient hash functions are desined to store sparse tensor data. The hash functions convert a higher order tensor to a matrix by tensor unfolding. And using the unfolded matrix, we develop algorithms to store nonzero data based on sparse fibers. We call our scheme Unfolded Compressed Row/Column Fiber (UCRF/UCCF). Our approach experimental result shows superior performance with standard dataset comparing to other important approaches.
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Index Terms
- Sparse tensor storage by tensor unfolding
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