Fusion of Adaptive Local Linear Embedding and Spectral Clustering Algorithm with Application to Fault Diagnosis
Fusion of Adaptive Local Linear Embedding and Spectral Clustering Algorithm with Application to Fault Diagnosis
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自适应局部线性嵌入和谱聚类算法的融合及其在故障诊断中的应用
DOI：
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发表时间：
2010
影响因子：
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通讯作者：
Zhang, Yulin
中科院分区：
文献类型：
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作者：
Zhuang, Jian;Wang, Na;Wang, Sun'an;Zhang, Yulin
针对高维非线性数据，在高维观测空间的模式识别中，引入了一种改进的流形学习算法，提出了一种自适应局部线性嵌入（ALLE）与递归应用归一化割算法（RANCA）相结合的新方法，利用自适应局部线性嵌入算法对原始数据进行非线性降维，对UCI标准数据集的仿真结果表明，该方法能够成功地将高维数据映射到低维本征空间，解决了传统方法对数据集结构依赖性较大的问题，在TEP（tennessee-eastman process）上的实验结果也证明了该方法在故障模式识别中的可行性和有效性。
Focusing on the data with high dimensions and nonlinearity,in pattern recognition in high dimensional observation space,an improved manifold learning algorithm is introduced,and a new approach is proposed by combining adaptive local linear embedding(ALLE) and recursively applying normalized cut algorithm(RANCA).The adaptive local linear embedding algorithm is employed for nonlinear dimension reduction of original dataset,then recursively applying normalized cut algorithm is used in clustering of low dimensional data.The simulation results of three UCI standard datasets show that the new method can map high-dimensional data into low-dimensional intrinsic space successfully,solves the more dependence on the structure of datasets in the traditional methods,and the classification accuracy and robustness of spectral clustering algorithm are remarkably improved.The experiment results on tennessee-eastman process(TEP) also demonstrate the feasibility and effectiveness of the new method in fault pattern recognition.