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- 摘要核主成分分析方法是主成分分析的改进算法，其采用非线性方法提取主成分。KPCA extracting principal component with nonlinear method is an improved conventional PCA.
- 主成分分析法及模糊综合评判法用于医院科室效益评价的对比Comparison analysis of synthetic benefit evaluation for departments in hospital using principal components analysis and ambiguity synthetic judgment
- 腐乳感官和理化品质的核主成分分析Kernel principal composition analysis of sensory and physiochemical quality of fermented bean curd
- 组合类别信息的核主成分分析（CIKPCA）Class-Information-Incorporated Kernel Principal Component Analysis (CIKPCA)
- 基于核主元分析法和支持向量机的人耳识别Ear recognition based on kernel principal component analysis and support vector machine
- 主成分分析法。Subject the matrix to principal components analysis.
- 核主元分析法Kernel principal component analysis
- 核主成分分析KPCA
- 核主分量分析法Kernel-based principal component analysis
- 全局主成分分析法global principal components analysis
- 基于主成分分析的核Fisher判别方法在油水识别中的应用Application of kernel Fisher method based on primary factor analysis to recognition problem between oil layer and water layer
- 主成分因子分析法main factor analysis
- 核主成分聚类分析Kernel principal component-cluster analysis
- 改进主成分分析法improved methods of principal component analysis
- 核的主成分分析kemel component analysis
- 核函数主成分分析Kernel principal component analysis
- 二维主成分分析法2DPCA
- 核主成分分析与传统主成分分析相比，可以得到更好的适合分类的特征。So KPCA can extract the feature set more suitable in categorization than classical conventional PCA.
- 时序主成分分析法times series analysis & PCA
- 基于核主成分分析与最小二乘支持向量机结合处理时间序列预测问题Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems
