.png){kind=icon}
.png){kind=icon}
Return
A Computationally Efficient Tensor Completion Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2018.2852490.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We introduce a tensor completion algorithm that uses a group-sparse regularizer with respect to the PARAFAC factors and is based on an optimization scheme that alternatingly minimizes a quadratic upper bound of the associated cost function. The proposed scheme allows matrixwise updates of the PARAFAC factors and, thus, leads to an efficient and scalable iterative algorithm, suitable for big-data applications. Experiments conducted on both synthetic and real data, corroborate the superior performance, in terms of runtime, of the proposed algorithm as compared with the other state-of-the-art approaches.
Keywords:
BSUM framework
group-sparse regularization
PARAFAC decomposition
tensor completion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W