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用主成分分析与神经网络进行人脸的识别
文件是整个的ＭＡＴＬＡＢ数据文件-using principal component analysis and neural networks face identification document is the entire data file MATLAB
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用于图像识别和特征提取时的主成分分析程序，采用Matlab编写。-for image recognition and feature extraction of principal component analysis procedures, the preparation of Matlab.
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PCA---主成分分析 LDA---线性区别分析此类实现结合两者的有缺点实现图像模式识别,其中需要有矩阵类-PCA principal component analysis --- --- LDA linear discriminant analysis combining the two to achieve such a flawed it Image is pattern recognition, which requires matrices
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结合PCA+LDA的图像识别算法VC封装类,PCA（主元素分析，光照敏感）,可用于人脸识别的初级算法-combination of image recognition algorithm VC Packaging category, PCA (principal component analysis, Light-sensitive), can be used for the initial face recognition algorithm
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利用vc实现图像的融合,包括一些经典的图像融合算法和主成分分析实现图像的融合.-use vc image fusion including some classic image fusion algorithms and principal component analysis image integration.
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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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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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主元分析 (Principal Component Analysis, PCA) 又叫：Karhunen-Loeve变换
(KLT)、Hotelling变换。
假设已经从图象已经缩放为N*M大小。
m幅N*M大小的图象Xi作为n*1列向量看待-PCA (Principal Component Analysis, PCA) also known as: Karhunen-Loeve Transform (KLT), Hotelling transform.
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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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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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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 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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一个非常通俗易懂的主成分分析教程，给出了主成分分析在计算机视觉中的应用，最后给出了主成分分析的代码。-A very easy to understand tutorial principal component analysis, principal component analysis is given in the application of computer vision, and finally gives the principal component analysis of the
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Robust principal component analysis论文-Robust principal component analysis
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matlab主成分分析，含完整主成分分析方法以及matlab源代码-matlab principal component analysis, including principal component analysis and the complete source code matlab
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论文Matlab实现：S. Pyatykh, J. Hesser, and L. Zheng, "Image Noise Level Estimation by Principal Component Analysis", IEEE Transactions on Image Processing, Volume: 22, Issue: 2, Pages: 687 - 699, February 2013.
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