Abstract
In this paper, the low-complexity tensor completion (LTC) scheme is proposed to improve the efficiency of low-rank tensor completion with competitive performance, which consists of the smooth matrix factorization (SMF) model and the corresponding alternating direction method of multiples (ADMM)-based solution. As for the SMF model, on one hand, we adopt the matrix factorization into the model of low-rank tensor completion for complexity reduction. On the other hand, we introduce the smoothness by total variation regularization and framelet regularization to guarantee the completion performance. To solve the SMF model, an ADMM-based solution is further proposed to realize the efficient and effective low-rank tensor completion. Finally, simulation results are presented to confirm the system gain of the proposed LTC scheme in both efficiency and effectiveness.
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Tang, L., Yang, C., Wang, Z., Zhang, X. (2022). A Low-complexity Tensor Completion Scheme Combining Matrix Factorization and Smoothness. In: Jain, L.C., Kountchev, R., Hu, B., Kountcheva, R. (eds) Wireless Technology, Intelligent Network Technologies, Smart Services and Applications. Smart Innovation, Systems and Technologies, vol 258. Springer, Singapore. https://doi.org/10.1007/978-981-16-5168-7_8
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DOI: https://doi.org/10.1007/978-981-16-5168-7_8
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