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Optimal kernel selection in kernel Fisher discriminant analysis
Kernel linear discriminant high dimensional feature space the implicit definition the kernel
2015/8/10
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance of KFDA depends on t...
Kernel-kNN: 基于信息能度量的核k-最近邻算法
距离度量 非线性变换 k-最近邻(k-NN) 核方法
2010/12/28
提出一种核k最近邻算法. 首先给出用于最近邻学习的信息能度量方法, 该方法克服了高维数据不便于用传统距离度量表示的困难, 提高了数据间类别相似性和距离的一致性. 在此基础上, 将传统的kNN扩展为非线性形式, 并采用半正定规划学习全局最优的度量矩阵. 算法主要特点是: 能较好地适用于高维数据, 并有效提升kNN 的分类性能. 多个数据集的实验和分析表明, 本文的Kernel-kNN算法与传统的kN...
Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Principal Component Analysis
Multiblock kernel methods Nonlinear Component Analysis Process Monitoring Fault detection
2009/9/9
In this paper, a multiblock kernel principal component analysis (MBKPCA) algorithm is proposed. Then a new fault detection and diagnosis approach based on MBKPCA is proposed to monitor large-scale pro...