关键词: 大数据, 随机跳跃, Web对象, 标签分类, 自动标注

Abstract: In order to solve the problem of time consuming and insufficient for labeling data, which leads the low computational efficiency in multi-label classification of Web objects, this paper proposes a multi-label classification algorithm based on sparse mixed graph random jump transition strategy for Web object. Firstly, based on the construction of the Web object affinity graph and tag correlation, weight adaptive method is used to construct a hybrid graph of Web object label classification, which realizes the automatic annotation of semi-supervised form and solves the time consuming problem of manual annotation; Secondly, in order to solve the problem of mixed graph, the random jump transition strategy is used to get the probability distribution between the mixed graph and the prediction  tag, which realizes the probability estimation of the class label of the unlabeled Web object and obtains the highest top-k correlation score; Finally, through the test on UCI Web dataset and real big data, the results show that the Rand index of the proposed algorithm is better than the selected contrast algorithms, which verifies the effectiveness of the proposed algorithm.

Key words: big data, random jump, Web object, label classification, automatic marking