中圖分類號： TP277 文獻(xiàn)標(biāo)識碼： A DOI： 10.19358/j.issn.2096-5133.2021.12.005 引用格式： 夏麗莎,，劉兵. 基于加權(quán)判別隨機(jī)鄰域嵌入的故障特征提取算法[J].信息技術(shù)與網(wǎng)絡(luò)安全,，2021，40(12)：26-31,，39.
Fault feature extraction method based on weighted discriminative stochastic neighbor embedding
Xia Lisha1,，Liu Bing2
(1.School of Business，University of Shanghai for Science and Technology,，Shanghai 200093,，China； 2.School of Information Science and Engineering,，Wuhan University of Science and Technology,，Wuhan 430081，China)
Abstract： In this paper, considering the high dimensionality, strong non-linearity, noise sensitivity, fault feature information redundancy and category label accessibility for big data, a novel method named Weighted Discriminative Stochastic Neighbor Embedding(WDSNE) is proposed for fault features extraction. This WDSNE method is an improvement based on the t-SNE unsupervised manifold learning method for non-linear data. Firstly, the data similarity between the original high-dimensional space and corresponding low-dimensional subspace is defined together with category information. Secondly, the Manhattan distance is selected as the distance measure in order to enhance the relative distance difference. Thirdly, the weighted data similarity is re-defined according to the Manhattan distance distribution. As a result, the class label information can be fully utilized as constraints to guide dimensionality reduction. This will make the inter-class more decentralized and the intra-class more compact. Experiments based on both UCI dataset and KDD99 network fault dataset demonstrate the diagnosis effectiveness of the improved fault features extraction method.
Key words : category information,；stochastic neighbor embedding,；weighted distance；fault features extraction
