基于局部特征提取的有监督的流形学习方法

doi:10.3901/JME.2017.24.211

机械工程学报 ›› 2017, Vol. 53 ›› Issue (24): 211-220.doi: 10.3901/JME.2017.24.211

• 交叉与前沿 • 上一篇

基于局部特征提取的有监督的流形学习方法

黎敏^1,2, 杨孟瑶¹, 陈泽¹, 赵启东¹

1. 北京科技大学机械工程学院北京 100083;
2. 北京科技大学钢铁共性技术协同创新中心北京 100083

收稿日期:2016-12-15 修回日期:2017-06-25 发布日期:2017-12-20
通讯作者: 黎敏(通信作者),女,1980年出生,博士,副教授,硕士研究生导师。主要研究方向为产品质量建模与监控、信号处理与模式识别。E-mail:limin@ustb.edu.cn
基金资助:
十二五科技支撑计划（2015BAF30B01）和省部共建耐火材料与冶金国家重点实验室开放基金（G201704）资助项目。

Supervised Manifold Learning Method Based on Local Feature Extraction

LI Min^1,2, YANG Mengyao¹, CHEN Ze¹, ZHAO Qidong¹

1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083;
2. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083

Received:2016-12-15 Revised:2017-06-25 Published:2017-12-20

摘要/Abstract

摘要： 为了提高工业产品的质量，提出一种基于局部特征提取的有监督的流形学习方法，用于工业生产过程中工艺参数的调整和优化。利用“多类邻域搜索”策略对每个样本点寻找邻域矩阵，对邻域矩阵进行特征分解，获得每个样本点的演化矢量，进而可以求得潜含在数据内部的主流形。另一方面，利用训练样本建立基于支持矢量数据描述的监控模型，对工艺过程进行监控。当发现异常样本时，将异常样本在主流形方向上进行投影，可以得到各个工艺参数的调整量，由此可将异常样本调控回到生产受控状态。利用IF钢实际生产过程数据进行验证，结果表明：新方法能有效提取出数据内部的流形结构，并通过主流形实现对工艺参数的调整，为实际生产过程提供了一种新的工艺参数优化方法。

关键词: 产品质量建模, 工艺参数优化, 局部特征提取, 有监督流形学习

Abstract: A supervised manifold learning method based on local feature extraction is proposed to improve the quality of industrial product. A new neighborhood search based on multi-class is used to obtain neighborhood matrices. The eigenvalue decomposition is applied to extract the manifold hidden in the data from the neighborhood matrices. At the same time, a monitoring model is built with the training data based on support vector data description (SVDD). If an abnormal sample is detected by SVDD, it will be projected on the manifold to obtain the adjustment values, which make the abnormal sample return to the normal state. The actual production process data of IF steel is conducted to verify the effectiveness of the proposed method. The results show that the new method can extract the essential manifold and optimize the process parameters effectively. So the proposed method can provide a new strategy to optimize the process parameters for the actual production process.

Key words: local feature extraction, parameters optimization, product quality modeling, supervised manifold learning

中图分类号:

TP277

黎敏, 杨孟瑶, 陈泽, 赵启东. 基于局部特征提取的有监督的流形学习方法[J]. 机械工程学报, 2017, 53(24): 211-220.

LI Min, YANG Mengyao, CHEN Ze, ZHAO Qidong. Supervised Manifold Learning Method Based on Local Feature Extraction[J]. Journal of Mechanical Engineering, 2017, 53(24): 211-220.

参考文献

[1] 张绍辉, 李巍华. 基于特征空间降噪的局部保持投影算法及其在轴承故障分类中的应用[J]. 机械工程学报, 2014, 50(3):92-99. ZHANG Shaohui, LI Weihua. Locality preserving projections based on feature space denoising and its application in bearing fault classification[J]. Chinese Journal of Mechanical Engineering, 2014, 50(3):92-99.
[2] TENENBAUM J, SILVA V, LANGFORD J. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323.
[3] ROWEIS S, SAUL L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326.
[4] BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(15):1373-1396.
[5] ZHANG Zhenyue, ZHA Hongyuan. Principal manifolds and nonlinear dimensionality reduction via local tangent space alignment[J]. SIAM Journal of Scientific Computing, 2004, 1(26):313-338.
[6] HE Xiaofei, YAN Shuicheng, HU Yuxiao, et al. Face recognition using laplacian faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 3(27):328-340.
[7] ZHANG Tianhao, YANG Jie, ZHAO Deli, et al. Linear local tangent space alignment and application to face recognition[J]. Neurocomputing, 2007, 70(7-9):1547-1553.
[8] LAW M, JAIN A. Incremental nonlinear dimensionality reductionby manifold learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 3(28):337-391.
[9] GAO Quanxue, XU Hui, LI Yiying, et al. Two-dimensional supervised local similarity and diversity projection[J]. Pattern Recognition, 2010, 10(43):3359-3363.
[10] YANG Wankou, SUN Changyin, ZHANG Lei. A multi-manifold discriminant analysis method for image feature extraction[J]. Pattern Recognition, 2011, 8(44):1649-1657.
[11] BOZAS K, IZQUIERDO E. Discriminant pairwise local embeddings[C]//IEEE International Conference on Multimedia & Expo Workshops, 2013, 370:1-4.
[12] ORSENIGO C, VERCELLIS C. Kernel ridge regression for out-of-sample mapping in supervised manifold learning[J]. Expert Systems with Applications, 2012, 9(39):7757-7762.
[13] RADUCANU B, DORMAIKA F. A supervised non-linear dimensionality reduction approach for manifold learning[J]. Pattern Recognition, 2012, 45(6):2432-2444.
[14] HUA Qiang, BAI Lijie, WANG Xizhao, et al. Local similarity and diversity preserving discriminant projection for face and handwriting digits recognition[J]. Neurocomputing, 2012, 86(4):150-157.
[15] GAO Quanxue, MA Jingjie, ZHANG Hailin, et al. Stable orthogonal local discriminant embedding for linear dimensionality reduction[J]. IEEE Transactions on Image Processing, 2013, 22(7):2521-2531.
[16] RADUCANU B, DORMAIKA F. Embedding new observations via sparse-coding for non-linear manifold learning[J]. Pattern Recognition, 2014, 47(1):480-492.
[17] 王萌,孙树栋.基于优化核空间的制造过程质量分析算法[J]. 机械工程学报,2012, 48(22):182-188. WANG Meng, SUN Shudong. Optimized kernel space based algorithm for quality data analysis[J]. Journal of Mechanical Engineering, 2012, 48(22):182-188.
[18] YU Jianbo. Semiconductor manufacturing process monitoring using gaussian mixture model and Bayesian method with local and nonlocal information[J]. IEEE Transactions on Semiconductor Manufacturing, 2012, 25(25):480-493.
[19] LIU Yuanjui, CHEN Tao, YAO Yuan. Nonlinear process monitoring and fault isolation using extended maximum variance unfolding[J]. Journal of Process Control, 2014, 24(6):880-891.
[20] XIAO Zhibo, WANG Huangang, ZHOU Junwu. Robust dynamic process monitoring based on sparse representation preserving embedding[J]. Journal of Process Control, 2016, 40:119-133.
[21] TONG Chudong, SHI Xuhua, LAN Ting. Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis[J]. ISA Transactions, 2016, 65:407.
[22] JOHN S. Kernel methods for pattern analysis[M]. Cambridge:Cambridge University Press, 2006.
[23] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 5213-2008冷轧低碳钢板及钢带[S].北京:中国标准出版社,2009. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the Republic of China. GB/T 5213-2008 Cold rolled low carbon steel sheet and strip[S]. Beijing:Standards Press of China, 2009.

基于局部特征提取的有监督的流形学习方法

Supervised Manifold Learning Method Based on Local Feature Extraction

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