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6下载：
统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines,
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Face Recognition, Face Detection, Lausanne Protocol, 3D Face Reconstruction,
Principal Component Analysis, Fisher Linear Discriminant Analysis,
Locality Preserving Projections, Kernel Fisher Discriminant Analysis,Face Recognition, Face Detection, L
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核主成分分析算法KPCA 的matlab程序/代码
基于二维数据的。-kernel principal component analysis
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kpca 基于核主成分分析的源程序，有注释，希望对大家有帮助！-kpca based on kernel principal component analysis, source code, there are comments, want to help you!
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核主元分析法能充分利用核函数来解决非线性问题，具有很好的非线性逼近能力-Kernel principal component analysis can take advantage of the kernel function to solve nonlinear problems, with good nonlinear approximation ability
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一个很好的核主成分分析matlab程序应用举例。该程序是在前人的核主成分分析程序基础上做了适当的修改产生的，可用于多维数据的降维和压缩处理。-A good kernel principal component analysis matlab application procedures, for example. The program is in the predecessors of Kernel Principal Component Analysis based on the proce
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为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。-In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms,
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降维工具箱，包含主元分析（PCA）,核主元分析（KPCA）等。-Dimensionality reduction kit, including principal component analysis (PCA), Kernel Principal Component Analysis (KPCA) and so on.
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Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with
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这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值- ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techn
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为解决PCA不适合多指标综合分析中非线性主成分分析的问题 ,采用核主成分分析 (kpca)方法 ,对我国不同地区 16种腐乳的品质进行了综合评价。
-PCA is not suitable to address the many indicators of a comprehensive analysis of non-linear principal component analysis of the problem, using Kernel Principal Component An
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学习PCA即主成元分析不可不读的经典外文文献。有需要的朋友下~-PCA learning as the main element analysis that can not read the classic literature in foreign languages. In need of a friend of ~
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基于核函数的主分量分析法源代码，可用于人脸识别-Kernel-based principal component analysis source code, can be used for face recognition
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Kernel Principal Component Analysis
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一个号的核主成分分析的人脸识别算法，整个程序非常的清楚明了！-A number of kernel principal component analysis for face recognition algorithms, the whole process is very easy to understand!
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核主成分分析中使用多项式核函数时的MATLAB代码，有注释，易看懂。-Kernel Principal Component Analysis in the use of polynomial kernel function of the MATLAB code, annotated, easy read.
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Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.
We introduce kernel entropy component analysis (kernel ECA) as a new method
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Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a n
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基于核方法的主成分分析matlab源代码，比较经典，推荐学习。-Method based on kernel principal component analysis matlab source code, more classic, recommended learning.
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核函数的实现，核分析 和核主成份分析，核Fisher判别 的matlab实现-Nuclear function, nuclear analysis and kernel principal component analysis, kernel Fisher matlab implementation
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