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主要介绍主分量分析，怎样提取主要特征来重构原始信号。-Introduces the principal component analysis, extracting the main features of how to reconstruct the original signal.
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子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the
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详细介绍PCA（主成分分析），应用于人脸识别。PCA概念，发展，关键技术-Details on PCA (Principal Component Analysis), applied to face recognition. PCA concept, development, and key technologies
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图像融合算法，高通滤波法、IHS法、PCA主成分分析、小波融合、小波和IHS结合的融合方法-Image fusion algorithms, high-pass filtering, IHS method, PCA principal component analysis, wavelet fusion, wavelet and IHS fusion method combining
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I present an expectation-maximization (EM) algorithm for principal
component analysis (PCA).
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人脸识别：使用PCA方法，即主成分分析，区分人脸和非人脸。主要用于随即过程大作业。-Face Recognition: Using the PCA method, that is, principal component analysis, the distinction between face and non-human face. Then the process used mainly for large operations.
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Principal Component Analysis源码，程序附带selfdemo演示-pca code
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pca主成分分析算法matlab源码,用于人脸识别中。-pca principal component analysis algorithm matlab source code for Face Recognition.
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principal component analysis in one dimension
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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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This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transfo
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Principal Component Analysis
• Uses:
– Data Visualization
– Data Reduction
– Data Classification
– Trend Analysis
– Factor Analysis
– Noise Reduction
• Examples:
– How many unique “sub-sets” are in
the sample?
– How ar
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主成分分析法的超详细介绍，附有多个开发环境的程序，举了多个例子以供练习，编了一个小程序，很方便地进行主成分分析，非常方便实用··（强烈要求管理员给个高分，谢谢）-Principal Component Analysis of the ultra-detailed descr iption of the development environment with a number of procedures, to cite a number of examples for practice, fo
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Principal Component Analysis
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Matlab code for PCA principal component analysis
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Principal Component Analysis
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提出一种基于主分量分析和相融性度量的快速聚类方法。通过构造主分量空间将高维数据投影到两个主成分上
进行特征提取，每一个主分量都是原始变量的线性组合-Is proposed based on Principal Component Analysis and Measure of blending fast clustering method. Principal component space by constructing a high-dimensional data onto two p
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目标的雷达散射截面(RCS)包含了丰富的目标类别信息,如何有效利用目标RCS特征对空间目标的雷
达识别具有重要意义. 文中提取中心矩作为特征向量,采用主分量分析( PCA)进一步进行特征压缩,利用支撑矢
量-Target radar cross section (RCS) contains a wealth of objective categories of information, how the effective use of target RCS characteristi
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This toolbox includes routines for using principal component analysis (PCA) and independent component analysis (ICA) to extract cellular signals from imaging data sets. A full descr iption and validation of the method is provided in the paper, "Autom
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ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。
基本思想：
在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。
-Descr iption This is an example of the basic Active Shape Model (ASM) as introduced by Coot
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