Recovering network traffic data from incomplete observed data becomes increasingly critical in network engineering and management. To fully exploit the spatial-temporal features of the Internet traffic data, this paper presents a new tensor completion model which combines the T-product-based tensor factorization with total variation (TV) regularization. To tackle the proposed model, an effective Proximal Alternating Minimization (PAM) algorithm with guaranteed convergence is designed. Extensive experiments on the real-word traffic datasets show that the proposed method has superiority over the existing state-of-the-art methods.
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