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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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I present an expectation-maximization (EM) algorithm for principal
component analysis (PCA).
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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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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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SVM PCA (Principal Component Analysis) algorithm has been widely used in engineering and science research, This report mainly from the PCA and the basic structure of the basic tenets of its research, Conventional PCA algorithm used mainly linear algo
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Teaching about pca - principal component analysis. you can learn how pca work and what eigenfaces is?
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主成分分析，支持向量机介绍，PDF文档，自制-Principal component analysis, support vector machine, PDF documents, homemade
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In order to fulfill the implementation of this project there are three main objectives that need to be achieved:
1. To learn and apply a technique for object recognition for single and multiface detection.
2. To examine the principal compon
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主成分分析，load数据可运行，有注释，简单易懂。-Principal component analysis，loading the data can run program.
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主成分分析相关文献、PPt理论讲解与实例-The principal component analysis of the relevant literature, PPt explanation and examples
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Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal component
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Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper,
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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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使用Matlab实现的关于主分量分析PCA的方法的案例，可先实现仿真-Matlab achieved using principal component analysis PCA on the case method, the simulation can be achieved
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人脸识别经典方法,主成分分析（PCA），线性鉴别分析（LDA）-Face the classical method, principal component analysis (PCA), linear discriminant analysis (LDA)
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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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