New Method of Fault Feature Extraction Based on Supervised LLE

摘要：

The Locally Linear Embedding (LLE) is one of the efficient nonlinear dimensionality reduction techniques, which can be used to fault feature extraction. But it is not taking the class information of the data into account. In this paper, we propose a novel approach of feature extraction based on supervised LLE algorithm. Via utilizing class information to guide the procedure of nonlinear mapping, the Supervised LLE enhances local within-class relations and help to classification. The approach uses the Supervised LLE to extract feature for class labels data, and utilizes RBF network to map the unlabeled data to the feature space, which easily implement fault pattern classification. The experiments on benchmark dataset and engineering instance demonstrate that, the proposed approach excels compared to PCA and LLE, and it is an accurate technique for classification.

关键词： Feature extraction Supervised LLE Nonlinear dimensionality reduction

作者: Quansheng Jiang Jiayun Lu Minping Jia

作者单位: Department of Physics, Chaohu University, Chaohu 238000, China School of Mechanical Engineering, Sou Department of Physics, Chaohu University, Chaohu 238000, China School of Mechanical Engineering, Southeast University, Nanjing 211189, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 1727-1731

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）