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主成分分析和偏最小二乘SquaresPrincipal成分分析（ PCA ）和偏最小二乘（ PLS ） ，广泛应用于工具。此代码是为了显示他们的关系，通过非线性迭代偏最小二乘（ NIPALS ）算法。
,Principal Component Analysis and Partial Least SquaresPrincipal Component Analysis (PCA) and Partial Least Squares (PLS) are widely used tools. Thi
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利用主成分分析方法，对TE模型产生的故障数据故障1进行故障检测-Using principal component analysis, on the TE model failure data generated by a fault detection fault
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高维PCA
参考文献：
MPCA Multilinear Principal Component Analysis of Tensor Objects-High-dimensional PCA References: MPCA Multilinear Principal Component Analysis of Tensor Objects
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PCA主成分分析，用于人脸识别，特征提取等-PCA principal component analysis for face recognition, feature extraction, etc.
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PCA代码
主成分分析代码
适合初学人脸识别的朋友学习使用-PCA principal component analysis source code suitable for beginner learning to use face recognition friend
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这是一个主成分分析的matlab程序，非常有用。-This is a principal component analysis of matlab procedures, very useful.
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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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它是SourceForge上的一个开源项目，使用Malib实现实时处理，CSU Face Identification Evaluation System进行人脸识别。算法包括：主成份分析(principle components analysis (PCA)),a.k.a eigenfaces算法,混合主成份分析,线性判别分析(PCA+LDA),图像差分分类器(IIDC),弹性图像匹配算法(EBGM)等等
Malic is realtime face recognition system
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使用Fuzzy Cluster Mean (FCM)與Principal component analysis (PCA)分類Yeast Data-Yeast data will be classified by means of Fuzzy Cluster Mean (FCM)and Principal component analysis (PCA) based on matlab.
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PCA 主成分分析在人脸识别中的应用 基于主成分分析理论对不同人脸库进行学习 总结“经验”并将“经验”用于对人脸的识别中-PCA Principal Component Analysis for Face Recognition Based on principal component analysis theory of different learning face database summary of " experience" and " experience
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主成分分析，人脸识别，模式识别，对图像处理有点帮助-Principal component analysis, face recognition, pattern recognition, image processing for a little help
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This programme is to calculate the principal component analysis (PCA) of a image. Naturally it is used to reduce the dmentionality of the image.
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26页PPT，详细介绍主成分分析（Principal Component Analysis，PCA）的概念-26 PPT, details of principal component analysis (Principal Component Analysis, PCA) concept of
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神经计算的实验作业。用principle components analysis计算模式的主分量。提取线性输入的特征。-Neural computing experiment operations. Computing model using principle components analysis of the principal component
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用Matlab实现主成分分析（PCA)算法.-Principal component analysis using Matlab implementation (PCA) algorithm.
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一个关于主成分分析的matlab演示，十分有用-A principal component analysis on matlab demo, very useful
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用于主成分分析（PCA）,包括，原变量相关系数矩阵的特征向量和特征值的求解，主成分的提取，载荷值的确定等-Used principal component analysis (PCA), including the original variable correlation matrix eigenvectors and eigenvalues of the solution, the main component of the extract, load value
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主成分分析法实现图像压缩,主要是应用主成分分析来进行图像信息的压缩处理和重构。-
Principal component analysis (pca) to realize image compression, is mainly used principal component analysis for image compression processing, and refactoring.
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The key concept in principal component analysis (PCA) is to reduce a high dimensional data volume into a lower dimensional space, where the low dimensional data con-tams most of the useful information/variance contained in the original data set. The
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Principal Component Analysis(PCA)主成分分析，Matlab实例代码，主成分分析-Principal Component Analysis(PCA)，matlab
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