Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance de...Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.展开更多
针对高光谱图像存在“维数灾难”的问题,提出一种全局判别与局部稀疏保持的高光谱图像半监督特征提取算法(GLSSFE)。该算法通过LDA算法的散度矩阵保存有类标样本的全局类内判别信息和全局类间判别信息,结合利用半监督PCA算法对有类标和...针对高光谱图像存在“维数灾难”的问题,提出一种全局判别与局部稀疏保持的高光谱图像半监督特征提取算法(GLSSFE)。该算法通过LDA算法的散度矩阵保存有类标样本的全局类内判别信息和全局类间判别信息,结合利用半监督PCA算法对有类标和无类标样本进行主成分分析,保存样本的全局结构;利用稀疏表示优化模型自适应揭示样本数据间的非线性结构,将局部类间判别权值和局部类内判别权值嵌入半监督LPP 算法保留样本数据的局部结构,从而最大化同类样本的相似性和异类样本的差异性。通过1-NN和SVM两个分类器分别对Indian Pines和Pavia University 两个公共高光谱图像数据集进行分类,验证所提特征提取方法的有效性。实验结果表明,该GLSSFE算法最高总体分类精度分别达到89.10%和92.09%,优于现有的特征提取算法,能有效地挖掘高光谱图像的全局特征和局部特征,极大地提升高光谱图像的地物分类效果。展开更多
LSPE(Locality and Similarity Preserving Embedding)特征选择算法首先基于KNN定义图结构来保持数据的局部性,再基于定义图学习数据的低维重构系数来保持数据的局部性和相似性。两个步骤独立进行,缺乏交互。由于近邻个数是人为定义的,...LSPE(Locality and Similarity Preserving Embedding)特征选择算法首先基于KNN定义图结构来保持数据的局部性,再基于定义图学习数据的低维重构系数来保持数据的局部性和相似性。两个步骤独立进行,缺乏交互。由于近邻个数是人为定义的,使得学习到的图结构不具备自适应的近邻,不是最优的,进而影响算法性能。为优化LSPE算法的性能,提出改进的局部和相似性保持特征选择算法,将图学习与稀疏重构、特征选择并入同一个框架,使得图学习和稀疏编码同时进行,其要求编码过程是稀疏的,自适应近邻的和非负的。所提算法旨在寻找一个能保持数据的局部性和相似性的投影,并对投影矩阵施加l(2,1)范数,进而选择能够保持局部性和相似性的相关特征。实验结果表明,改进后的算法减少了主观人为影响,消除了选择特征的不稳定性,对数据噪声鲁棒性更强,提高了图像分类的准确率。展开更多
基金Supported by the National Natural Science Foundation of China (61273160), the Natural Science Foundation of Shandong Province of China (ZR2011FM014) and the Fundamental Research Funds for the Central Universities (10CX04046A).
文摘Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.
文摘针对高光谱图像存在“维数灾难”的问题,提出一种全局判别与局部稀疏保持的高光谱图像半监督特征提取算法(GLSSFE)。该算法通过LDA算法的散度矩阵保存有类标样本的全局类内判别信息和全局类间判别信息,结合利用半监督PCA算法对有类标和无类标样本进行主成分分析,保存样本的全局结构;利用稀疏表示优化模型自适应揭示样本数据间的非线性结构,将局部类间判别权值和局部类内判别权值嵌入半监督LPP 算法保留样本数据的局部结构,从而最大化同类样本的相似性和异类样本的差异性。通过1-NN和SVM两个分类器分别对Indian Pines和Pavia University 两个公共高光谱图像数据集进行分类,验证所提特征提取方法的有效性。实验结果表明,该GLSSFE算法最高总体分类精度分别达到89.10%和92.09%,优于现有的特征提取算法,能有效地挖掘高光谱图像的全局特征和局部特征,极大地提升高光谱图像的地物分类效果。
文摘LSPE(Locality and Similarity Preserving Embedding)特征选择算法首先基于KNN定义图结构来保持数据的局部性,再基于定义图学习数据的低维重构系数来保持数据的局部性和相似性。两个步骤独立进行,缺乏交互。由于近邻个数是人为定义的,使得学习到的图结构不具备自适应的近邻,不是最优的,进而影响算法性能。为优化LSPE算法的性能,提出改进的局部和相似性保持特征选择算法,将图学习与稀疏重构、特征选择并入同一个框架,使得图学习和稀疏编码同时进行,其要求编码过程是稀疏的,自适应近邻的和非负的。所提算法旨在寻找一个能保持数据的局部性和相似性的投影,并对投影矩阵施加l(2,1)范数,进而选择能够保持局部性和相似性的相关特征。实验结果表明,改进后的算法减少了主观人为影响,消除了选择特征的不稳定性,对数据噪声鲁棒性更强,提高了图像分类的准确率。