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Description: PCA/LDA/LPP/TensorLPP/代码。
LPP是目前一种比较重要的子空间算法。基于Tensor的子空间算法，是传统PCA/LDA算法的进一步推广，具有重要意义。-PCA / LDA / LO / TensorLPP / code. Alignment is a more important subspace algorithm. Based on the Tensor subspace algorithm, is a traditional PCA / LDA further promote algorithm is of great significance.
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Size: 16351 |
Author: 李民 |
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Description: 子空间分解matlab程序。采用PCA主成元分析方法。
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Size: 894 |
Author: shiyu |
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Description: 本文是2011年eslvier上的关于自适应局部背景特征建模的一篇论文。本文介绍了关于局部PCA学习背景变化的光照，能对背景的全局或局部的变化作出最快速的响应
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Author: tinquan |
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Description: 模式识别PCA(principle component analysis)源码.matlab 格式。PCA为经典而且经常使用的算法。-pattern recognition PCA (principle component analysis) source. Matlab format. PCA to the classic and often use the algorithm.
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Author: 吴东 |
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Description: PCA/LDA/LPP/TensorLPP/代码。
LPP是目前一种比较重要的子空间算法。基于Tensor的子空间算法，是传统PCA/LDA算法的进一步推广，具有重要意义。-PCA/LDA/LO/TensorLPP/code. Alignment is a more important subspace algorithm. Based on the Tensor subspace algorithm, is a traditional PCA/LDA further promote algorithm is of great significance.
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Size: 16384 |
Author: 李民 |
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Description: 子空间分解matlab程序。采用PCA主成元分析方法。-Subspace Decomposition matlab procedures. Using PCA Principal Component Analysis.
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Size: 1024 |
Author: shiyu |
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Description: Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.-Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA (X, PPCA_DIM) computes the principal component subspace U of dimension PPCA_DIM using a centred covariancematrix X. The variable VAR contains the off-subspace variance (whichis assumed to be spherical ), while the vector LAMBDA contains thevariances of each of the principal components. This is computedusing the eigenvalue and eigenvector decomposition of X.
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Author: 西晃云 |
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Description: 在臉部的相關研究上，PCA方法常被使用在次空間中用以定義最佳的臉部樣式表現方式。其利用一些訓練用的臉部影像集合來產生本徵臉，並將臉部空間加以延伸使這些影像中的臉部區域會被投影到影像的次空間(subspace)並加以叢集化。 同樣的非臉部區域的訓練影像，亦會使用相同方法投影到相同的空間並加入叢集化。之後這兩個投影的次空間可以經由比較的方式，得出臉部區域與非臉部區域在次空間投影上的分佈情形。 -pca_transformation
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Author: 陳玉芬 |
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Description: 子空间学习的代码，主要包括人脸识别中常用的特征提取算法如pca lda 以及目前常见的流行学习的相关代码-Subspace learning the code, mainly including commonly used in face recognition feature extraction algorithms such as pca lda and the current prevalence of common learning-related code
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Author: dd |
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Description: Eigenfaces: PCA tends to find a p-dimensional subspace
whose basis vectors correspond to the maximum
variance direction in the original image space (p N).
We called the new subspace defined by basis vectors “face
space”. First, all training faces are projected onto the face
space to find a set of weights that describes the contribution
of each vector. Then we project all testing faces onto the
face space to obtain a set of weights. Finally, we identify
the face by comparing a set of weights for the testing face
to sets of weights of training faces.
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Size: 7017472 |
Author: sam |
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Description: Eigenfaces: PCA tends to find a p-dimensional subspace
whose basis vectors correspond to the maximum
variance direction in the original image space (p N).
We called the new subspace defined by basis vectors “face
space”. First, all training faces are projected onto the face
space to find a set of weights that describes the contribution
of each vector. Then we project all testing faces onto the
face space to obtain a set of weights. Finally, we identify
the face by comparing a set of weights for the testing face
to sets of weights of training faces.
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Size: 11264 |
Author: sam |
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Description: This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It can well characterize the local scatter as well non-local scatter which simultaneously maximizes the non–local scatter and minimizes the local scatter. This makes UDP more powerful and more intuitive than LDA and PCA. This makes UDP a good choice for real-world biometrics application The proposed method is applied to palm biometrics and is examined by small set of samples per class.
-This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It can well characterize the local scatter as well non-local scatter which simultaneously maximizes the non–local scatter and minimizes the local scatter. This makes UDP more powerful and more intuitive than LDA and PCA. This makes UDP a good choice for real-world biometrics application The proposed method is applied to palm biometrics and is examined by small set of samples per class.
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Author: hello |
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Description: principal component analysis (PCA ) is a well known approach for dimensionality reduction of the feature space. It has been successfully applied in face recognition. The main idea is to decompose face images into a small set of feature images called eigenfaces, which can be considered as points in a linear subspace called “face space” or “eigenspace”
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Size: 2048 |
Author: omid |
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Description: PCA人脸识别算法，子空间的方法之一，比较基础-PCA face recognition algorithm, one of the subspace method and basis of comparison
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Size: 2048 |
Author: 杨宁 |
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Description: 对人脸图像进行主成份分析，并显示训练子空间的特征脸。-Principal component analysis of face images, and displays the characteristics of the training subspace face.
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Author: gloria |
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Description: MATLAB FACE RECOGINION,讲述了MATLAB，在人脸识别中的应用，主要基于子空间的PCA方法。-The MATLAB FACE RECOGINION, about MATLAB, in face recognition, PCA method is mainly based on subspace.
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Author: wuhaojun |
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Description: Set of files about subspace method and pca
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Author: bernardo |
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Description: Fisherface方法的实现是在PCA数据重构的基础上完成的，首先利用PCA将高维数据投影到低维特征脸子空间，然后再在这个低维特征脸子空间上用LDA特征提取方法得到Fisherface。程序中使用参数寻优的方法来寻找最佳投影维数，以达到比较理想的识别效果。-The Fisherface method implemented in the PCA data reconstruction based on the completion of the first use of PCA projection of high-dimensional data to a low dimensional feature subspace, and then on the characteristics of low-dimensional subspace LDA feature extraction methods to get the Fisherface. Program parameter optimization method is used to find the best projection dimension, in order to achieve the ideal identification.
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Description: 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 send to engalaatharwat@hotmail.com
Egypt - HICIT - +20106091638
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Size: 1226752 |
Author: Anusha |
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Description: 在MATLAB环境下的PCA的计算及相关程序，以及PCA的可视化程序-Principal components analysis,Companion function to pca, Generate random vectors in PCA subspace,
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Size: 26624 |
Author: 何其 |
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