国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Daily work, learning and life can be guaranteed by in-time diagnosis of network fault. Traditional methods, based on supervised learning, rely on a large number of discriminative data, which is difficult to be met in practice. To deal with those problems, our algorithm utilizes the principle component analysis to reduce the dimension of the features, and then constructs the laplacian matrix based on the new features. The corresponding matrix can describe the relationship between data well. On the basis of that, we design the objective function of transductive learning and apply the lagrange multiplier to optimize that. Experimental results verify the high accuracy of our algorithm while dealing with low number of labeled data. Our algorithm can significantly improve the diagnosis of network fault.