Efficient Representation to Dynamic QoS Data via Generalized Nesterov's Accelerated Gradient-incorporated Biased Non-negative Latent Factorization of Tensors

A biased non-negative latent factorization of tensors model can perform accurate representation learning to dynamic quality-of-service (QoS) data. It relies on a non- negative and multiplicative update on incomplete tensors learning algorithm. However, this algorithm suffers from slow convergence when the target data increases to perplex such model's learning objective. To address such challenging issue, this study incorporates a generalized Nesterov's accelerated gradient method into the mentioned learning algorithm, thereby achieving a new algorithm. Based on this algorithm, a new model is successfully constructed. Empirical studies on two industrial datasets indicate that this new model is superior to the state-of-the-art models in both computational efficiency and prediction accuracy for missing QoS data after performing presentation learning on dynamic QoS data. Hence, this work represents an important progress in the field of fast and accurate latent factorization of tensors.