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这是PCA主分量分析在matlab中的基本应用，可以理解和学习体会下,This is the principal component analysis PCA in matlab basic applications, and learning experience can be understood under the
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一篇很不错的关于人脸表情识别的论文。论文提出了一种基于人脸局部特征的表情识别方法，先选取人脸重要的局部特征,对得到的局部特征进行主成分分析,然后用支持向量机( SVM)设计局部特征分类器来确定测试表情图像中局部特征,同时设计支持向量机( SVM)表情分类器,确定表情图像的所属类别。-A very good facial expression recognition on paper. This paper proposes a feature based on local expression
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Robust principal component analysis for non-centered data
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I present an expectation-maximization (EM) algorithm for principal
component analysis (PCA).
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：独立成分分析 ( I C A)在国内尚属一门新型的方法 介绍了I C A的原理及其算法 ，然后介绍了该算法在盲源
信号分离中的具体应用，并将此方法 与主成分方洼 ( P C A)进行了比较-: Independent Component Analysis (ICA) in China is a new method to introduce the principle of the ICA and its algorithm, and then introduced the algori
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通过用列主元消去法编写程序，使学生掌握用列主元消去法解线性方程组的思想-Out by using principal component elimination method to write programs so that students master the use of out principal component elimination method solution of the ideological system of linear equations
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基于小波分析和主成分分析的人脸识别研究随着社会的发展，社会各个方面对快速有效的身份验证的要求日益迫切。由
于生物特征是人的内在属性，具有很强的自身稳定性和个体差异性，因此是身份
验证的理想依据。其中利用人脸特征又是最自然直接的手段，相比其他生物特征，
它具有直接、友好、方便的特点，易于为用户接受。从而，人脸识别吸引了越来
越多来自计算机视觉和信号处理等领域的关注，成为模式识别、图像处理等学科
的研究热点。-Based on wavelet analysis and princ
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介绍结合LDA与PCA算法的优化分类方法，提出了一般情况下LDA及PCA的计算方法-An Optimal Transformation for Discriminant and Principal Component Analysis
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辛苦收集的关于独立成分分析的ppt文档，可帮助你快速了解主成分分析的主要内容及发展-Hard to collect on the independent component analysis of ppt documents, can help you quickly understand the principal component analysis and development of the main content
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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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Probabilistic Principal Component Analysis
– Latent variable models
– Probabilistic PCA
• Formulation of PCA model
• Maximum likelihood estimation
– Closed form solution
– EM algorithm
» EM Algorithms for regular PCA
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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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for components in principal component analysis
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pdf for fingerprint from ieee include:
PIPELINED MINUTIAE EXTRACTION FROM FINGERPRINT IMAGES
A Novel Principal Component Analysis Neural Network Algorithm for Fingerprint Recognition
in Online Examination System
Processing of Distorted Finger
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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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在高光谱图像处理和分析中,主成分分析方法是使用最广泛的线性降维
方法之一,它概念简单,实现算法高效"在信号处理中,所谓的Karl血nen.b姆ve
变换实际上就是主成分分析
-In hyperspectral image processing and analysis, principal component analysis is the most widely used linear dimensionality reduction methods, it is conceptu
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主成分回归分析程序的研究,很具有参考价值，值得看看相信会有用-Research principal component regression analysis program, it is very valuable, it is worth a look I believe will be useful
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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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