摘要
针对数据维数高、非线性且从高维观测空间分析数据模式困难的问题,将改进的流形学习算法引入到数据聚类中,提出了一种结合自适应局部线性嵌入和递归调用规范切融合的新方法.采用自适应局部线性嵌入对原始数据进行非线性降维,应用递归调用规范切对低维空间数据进行聚类,通过对3组UCI标准测试数据集的仿真实验表明,新方法能够将高维数据有效地映射到低维本质空间,克服了传统方法对数据集结构的依赖性,从而显著提高了谱聚类算法分类的准确性和稳定性.同时,对于田纳西-伊斯曼过程的数据实验,表明了该方法对故障模式识别的可行性和有效性.
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.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2010年第1期77-82,共6页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金青年科学基金资助项目(50705073)
关键词
局部线性嵌入
谱聚类
递归调用规范切
故障诊断
local linear embedding
spectral clustering
recursively applying normalized cut
fault diagnosis
