论文标题
张量法的ADMM结合的潜在分解用于QoS预测
An ADMM-Incorporated Latent Factorization of Tensors Method for QoS Prediction
论文作者
论文摘要
随着互联网发展的迅速发展，从广泛的候选人中选择合适的Web服务很重要。服务质量（QOS）在服务消费者要求的服务方面动态地描述了Web服务的性能。此外，男高音（LFT）的潜在分解对于在高维和稀疏（HID）张量中发现时间模式非常有效。但是，当前的LFT模型的收敛速率低，很少考虑异常值的影响。为了解决上述问题，本文提出了一种乘数的交替方向方法（ADMM）基于张紧器模型的基于乘数的异常值非负性潜在物质化。我们通过使用ADMM优化框架构建增强的拉格朗日函数来维持模型的非负性。此外，凯奇功能被视为减少对模型训练的影响的公制函数。在两个动态QoS数据集上的经验工作表明，该方法在预测准确性上具有更快的收敛性和更好的性能。
As the Internet developed rapidly, it is important to choose suitable web services from a wide range of candidates. Quality of service (QoS) describes the performance of a web service dynamically with respect to the service requested by the service consumer. Moreover, the latent factorization of tenors (LFT) is very effective for discovering temporal patterns in high dimensional and sparse (HiDS) tensors. However, current LFT models suffer from a low convergence rate and rarely account for the effects of outliers. To address the above problems, this paper proposes an Alternating direction method of multipliers (ADMM)-based Outlier-Resilient Nonnegative Latent-factorization of Tensors model. We maintain the non-negativity of the model by constructing an augmented Lagrangian function with the ADMM optimization framework. In addition, the Cauchy function is taken as the metric function to reduce the impact on the model training. The empirical work on two dynamic QoS datasets shows that the proposed method has faster convergence and better performance on prediction accuracy.
