Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for ...Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.展开更多
支持向量机因其相比于传统算法具有良好的分类性能,而广泛地应用于故障诊断研究中。但标准SVM存在训练时间长,占用内存大的不足。近似支持向量机(Proximal Support Vec-tor Machines,PSVM)算法具有训练速度快占用内存少的特点,特别适用...支持向量机因其相比于传统算法具有良好的分类性能,而广泛地应用于故障诊断研究中。但标准SVM存在训练时间长,占用内存大的不足。近似支持向量机(Proximal Support Vec-tor Machines,PSVM)算法具有训练速度快占用内存少的特点,特别适用于大量数据的故障诊断。但其对于分类超平面附近点的诊断精度略显不足。针对此类问题文中将耗时较少的Vague-Sigmoid核函数应用于PSVM,用以提高其对于在分类面附近样本的分类精度,仿真证明获得了较好的效果。展开更多
针对化工过程数据中包含噪声和强非线性的特点,提出了基于小波去噪核主元分析(De-noised Kernel Principal Component Analysis,DKPCA)和邻近支持向量机(Proximal Support Vector Machine,PSVM)的性能监控和故障诊断新方法.将样本数据...针对化工过程数据中包含噪声和强非线性的特点,提出了基于小波去噪核主元分析(De-noised Kernel Principal Component Analysis,DKPCA)和邻近支持向量机(Proximal Support Vector Machine,PSVM)的性能监控和故障诊断新方法.将样本数据用小波方法进行去噪处理,去除数据所包含的噪声,通过KPCA将降噪后的数据进行变换,在特征空间里构建T2和Q统计量来监测是否有故障发生;若发生故障,则计算数据的非线性主元得分向量,并将其作为PSVM的输入值,通过PSVM分类来确定故障的具体类型.流化催化裂化装置(FCCU)仿真试验验证了小波去噪的必要性和利用DKPCA-PSVM进行监控和故障诊断的有效性.展开更多
当两类样本分布存在差异时,最接近支持向量机(Proximal Support Vector Machine,PSVM)等最小二乘类分类器分类结果将出现偏差,不能实现最小错误率分类.本文在分析PSVM等价广义特征值分解模型基础上,提出了一种改善原PSVM分类决策面的优...当两类样本分布存在差异时,最接近支持向量机(Proximal Support Vector Machine,PSVM)等最小二乘类分类器分类结果将出现偏差,不能实现最小错误率分类.本文在分析PSVM等价广义特征值分解模型基础上,提出了一种改善原PSVM分类决策面的优化样本分布PSVM,其基本思想是通过引入最大化正确分类样本距决策面距离,同时最小化错误分类样本距决策面距离的优化样本分布正则化项,构造优化样本分布PSVM的广义特征值分解模型.通过人工数据集和UCI数据集的10个数据子集上的对比实验,验证了该改进分类模型能够有效调整决策边界,从而获得更好的分类效果.展开更多
基金TheNationalHighTechnologyResearchandDevelopmentProgramofChina (No .86 3 5 11 930 0 0 9)
文摘Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
文摘支持向量机因其相比于传统算法具有良好的分类性能,而广泛地应用于故障诊断研究中。但标准SVM存在训练时间长,占用内存大的不足。近似支持向量机(Proximal Support Vec-tor Machines,PSVM)算法具有训练速度快占用内存少的特点,特别适用于大量数据的故障诊断。但其对于分类超平面附近点的诊断精度略显不足。针对此类问题文中将耗时较少的Vague-Sigmoid核函数应用于PSVM,用以提高其对于在分类面附近样本的分类精度,仿真证明获得了较好的效果。
文摘针对化工过程数据中包含噪声和强非线性的特点,提出了基于小波去噪核主元分析(De-noised Kernel Principal Component Analysis,DKPCA)和邻近支持向量机(Proximal Support Vector Machine,PSVM)的性能监控和故障诊断新方法.将样本数据用小波方法进行去噪处理,去除数据所包含的噪声,通过KPCA将降噪后的数据进行变换,在特征空间里构建T2和Q统计量来监测是否有故障发生;若发生故障,则计算数据的非线性主元得分向量,并将其作为PSVM的输入值,通过PSVM分类来确定故障的具体类型.流化催化裂化装置(FCCU)仿真试验验证了小波去噪的必要性和利用DKPCA-PSVM进行监控和故障诊断的有效性.
文摘当两类样本分布存在差异时,最接近支持向量机(Proximal Support Vector Machine,PSVM)等最小二乘类分类器分类结果将出现偏差,不能实现最小错误率分类.本文在分析PSVM等价广义特征值分解模型基础上,提出了一种改善原PSVM分类决策面的优化样本分布PSVM,其基本思想是通过引入最大化正确分类样本距决策面距离,同时最小化错误分类样本距决策面距离的优化样本分布正则化项,构造优化样本分布PSVM的广义特征值分解模型.通过人工数据集和UCI数据集的10个数据子集上的对比实验,验证了该改进分类模型能够有效调整决策边界,从而获得更好的分类效果.
