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搜索资源 - Principal component analysis PCA
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PCA:主元分析。进行降维,实现TE化工过程特征提取。-PCA: principal component analysis. Dimensionality reduction, feature extraction chemical process to achieve TE.
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这是一个用java编写的PCA程序,很好地完成了主成分分析。-This is a program written in java PCA, well done principal component analysis.
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基于PCA主成分分析的混沌时间序列嵌入维求解算法-Based on PCA principal component analysis of chaotic time series embedding dimension algorithm
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2DPCA,即二维主成分分析,相对于传统的PCA(主成分分析),2DPCA在对二维图像进行降维时不需要转成一维(向量)-2DPCA, ie two-dimensional principal component analysis, as opposed to the traditional PCA (Principal Component Analysis), 2DPCA in dimensionality reduction of two-dimensional images into one
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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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使用主成分分析法(PCA, Principal Component Analysis)进行颜色转换(Color Transfer)的Matlab程序-Function to implement the Color Transfer using PCA (Principal Component Analysis).
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丢失数据的主成分分析,处理不完整数据的pca分解。pcaMatlab-Principal component analysis of missing data, incomplete data processing pca decomposition.pcaMatlab
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主分量分析对SAR图像目标进行特征提取,用最近临方法进行分类-Principal component analysis(PCA) of SAR image target feature extraction, classification using nearest-neighbor method
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用于主成分分析的源码,由例子可以直接了解PCA的步骤方法-Principal component analysis of the source code used by the example of the method can be directly aware of the steps of PCA
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pca his code to apply PCA (Principal Component Analysis)
for any information please send to engalaatharwat@hotmail.com
Egypt - HICIT - +20106091638
-pca his code to apply PCA (Principal Component Analysis)
for any information please sen
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pca 人脸识别算法,主成分分析 ( Principal Component Analysis , PCA )是一种掌握事物主要矛盾的统计分析方法,它可以从多元事物中解析出主要影响因素,揭示事物的本质,简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间.-pac faces dectect for matlab
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采用PCA主元分析进行人脸识别, 模式识别-Using PCA principal component analysis for face recognition, pattern recognition
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Wavelet PCA for principal component analysis and noise reduction using wavelet methods.
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混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal compon
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運用PCA將高精度人臉的深度資料(FRGC database)分析主成分後重建低精度的Kinect深度資訊值,細化Kinect所產生的量測誤差。重建出較好的三維人臉模型-The depth of data using PCA face of precision (FRGC database) Principal component analysis of the reconstruction after the low accuracy of Kinect depth information v
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主元分析法(PCA)是目前基于多元统计过程控制的故障诊断技术的核心,是基于原始数据空间,通过构造一组新的潜隐变量来降低原始数据空间的维数,再从新的映射空间抽取主要变化信息,提取统计特征,从而构成对原始数据空间特性的理解。-Principal Component Analysis
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This code uses PCA(principal Component Analysis) approach for human face recognition System.
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这是一个PCA的练习题,即主成分分析,用于数据将维,将高维大数据降为低维数据。是一种高效率的数据处理方法。-This is a PCA exercises, namely principal component analysis for data-dimensional, high-dimensional data is reduced to a large low-dimensional data. Is a highly efficient method of data processing
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通过主成分分析和偏最小二乘分析,然后画出数据的PCA主轴和PLS主轴-Principal component analysis and partial least squares analysis, and then draw the PCA and PLS spindle axis data
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PCA神经网络及学习算法,简单的主分量分析-PCA neural networks and learning algorithms, a simple principal component analysis
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