Description: PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-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 algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example Platform: |
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Author:东方云 |
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Description: PCA（主成分分析）算法被广泛应用于工程和科学研究中，本报告主要从PCA的基本结构和基本原理对其进行研究，常规的PCA算法主要采用线性算法，通过研究论证发现线性的PCA算法存在着许多不足，比如线性PCA算法不能从线性组合中把独立信号成分分离出来，主分量只由数据的二阶统计量—自相关阵确定，这种二阶统计量只能描述平稳的高斯分布等，因此必须对其进行改进，经改进后的PCA算法有非线性PCA算法、鲁棒算法等。我们通过PCA算法在直线（平面）中拟和的例子说明了PCA在工程中的应用。本例子采用的是成分分析中的次成分（方差最小的成分），通过对结果的分析，我们可以看出，利用PCA算法可以得到较好的拟和结果。-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 algorithm, found through research and demonstration linear PCA algorithm, there are many inadequate, For example, not linear PCA algorithm from the linear combination of the independent signal components separated, PCA data only from the second-order statistics-auto-correlation matrix to determine, Such second-order statistics can only describe a smooth Gaussian distribution, it is necessary to improve it. After the improvement of the PCA algorithm is nonlinear PCA algorithm, robust algorithm. PCA algorithm we passed the line (plane), and to be example Platform: |
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Author:东方云 |
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Description: 关于matlab主元分析的一个简单的实例，可供初学者参考学习。-Matlab PCA on a simple example of reference for beginners to learn. Platform: |
Size: 3072 |
Author:刘明 |
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Description: matlab 主成分分析法程序，以目标威胁度评估为例-matlab principal component analysis procedures in order to target the threat assessment as an example Platform: |
Size: 1024 |
Author:shaseng |
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Description: 主成分分析程式碼. 內附帶一題有關:身高,體重,與性別的主成分分析經典例題,並附有詳細的程式說明. 此外,有關共變異數矩陣,特徵值,與特徵向量的求法,在本程式內均有程式步驟說明以供需求者能更輕易使用.-Principal components analysis code. Attached to one title: the height, weight, and gender example classical principal component analysis, together with a detailed description of the program. In addition, the covariance matrix, eigenvalue, with eigenvector method in this program are steps in the program description for the needs of those who can more easily use. Platform: |
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Author:worthwhile |
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Description: 一个很好的核主成分分析matlab程序应用举例。该程序是在前人的核主成分分析程序基础上做了适当的修改产生的，可用于多维数据的降维和压缩处理。-A good kernel principal component analysis matlab application procedures, for example. The program is in the predecessors of Kernel Principal Component Analysis based on the procedures have been adapted to do so generated can be used for dimensionality reduction of multi-dimensional data processing and compression. Platform: |
Size: 2048 |
Author:Liquanlin |
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Description: ua University, in 2002 publi
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Mailto US | Studio | Copyright Complaints Platform: |
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Author:wibisono |
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Description: 一种健壮的主成分分析算法，根据文献《ROBPCA: a New Approach to Robust Principal Component Analysis》-《ROBPCA: a New Approach to Robust Principal Component Analysis》 Platform: |
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Author:钱叶魁 |
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Description: 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。
-Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach.
Basic idea:
The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions.
After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour.
Literature:
- Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I Platform: |
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Author:谢冉 |
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Description: 有关图形图像的核主成分分析方法 一个很好的例子-Relevant graphic image of kernel principal component analysis method is a good example Platform: |
Size: 1024 |
Author:hanyue |
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Description: 一个有关OMP的应用的例子，适合初学者学习
正交匹配追踪方法-An example of application of relevant OMP, suitable for beginners
The principal component analysis method Platform: |
Size: 1024 |
Author:hanyue |
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Description: 计算机仿真用于主成分分析的ICA分析程序，算法举例和波形分析程序。-Computer simulation for analysis of principal component analysis of ICA procedures, algorithms and waveform analysis program for example. Platform: |
Size: 382976 |
Author:郭利君 |
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Description: PCA主成分分析算法用于人脸识别的例子。此算法是基于矩阵特征值的排序而提取出主要成分的。-PCA principal component analysis algorithm for face recognition example. This algorithm is based on the ranking of the matrix eigenvalue extracted principal components. Platform: |
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Author:白正彪 |
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Description: 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 uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. Platform: |
Size: 6144 |
Author:shiheb |
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Description: 利用matlab进行主成分分析，有现成的代码和数据，主成分分析例子-Use of matlab for principal component analysis, have a ready-made code and data, principal component analysis example
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Size: 8192 |
Author:苏夏 |
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