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Description: 基于K-L的人脸识别源代码和修改后的PCA进行人脸识别的Matlab源代码-based on K-L Face Recognition source code and modify the PCA for face recognition Matlab source code
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Size: 5324 |
Author: wh |
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Description: k-l和PCA算法在人脸识别中的具体实现(MATLAB)
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Size: 4028 |
Author: xyl |
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Description: 主成分分析方法（PCA），PCA算法的理论依据是K-L变换，通过一定的性能目标来寻找线性变换W，实现对高维数据的降维。
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Size: 1411 |
Author: 李伟 |
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Description: 算法介绍
矩阵求逆在程序中很常见，主要应用于求Billboard矩阵。按照定义的计算方法乘法运算，严重影响了性能。在需要大量Billboard矩阵运算时，矩阵求逆的优化能极大提高性能。这里要介绍的矩阵求逆算法称为全选主元高斯-约旦法。
高斯-约旦法（全选主元）求逆的步骤如下：
首先，对于 k 从 0 到 n - 1 作如下几步：
从第 k 行、第 k 列开始的右下角子阵中选取绝对值最大的元素，并记住次元素所在的行号和列号，在通过行交换和列交换将它交换到主元素位置上。这一步称为全选主元。
m(k, k) = 1 / m(k, k)
m(k, j) = m(k, j) * m(k, k)，j = 0, 1, ..., n-1；j != k
m(i, j) = m(i, j) - m(i, k) * m(k, j)，i, j = 0, 1, ..., n-1；i, j != k
m(i, k) = -m(i, k) * m(k, k)，i = 0, 1, ..., n-1；i != k
最后，根据在全选主元过程中所记录的行、列交换的信息进行恢复，恢复的原则如下：在全选主元过程中，先交换的行（列）后进行恢复；原来的行（列）交换用列（行）交换来恢复。-algorithm introduced in the matrix inversion process is very common, which are mainly used for Billboard matrix. In accordance with the definition of the method of calculating multiplication, seriously affecting the performance. The need for a large number of Billboard matrix operations, matrix inversion optimization can significantly improve performance. Here we introduce the matrix inversion algorithm called full-elected PCA Gauss-Jordan and France. Gauss-Jordan and France (all elected PCA) inversion of the following steps : First, for k from 0 to n-1 for the following steps : from the first trip k, k started out the bottom right corner Subarray largest absolute selected elements, and element remember meeting the line and out, the adoption OK exchange and the exchange out of its exchange
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Size: 3072 |
Author: 刘亮 |
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Description: 使用KL变换和主分量分析的人脸识别小程序，使用MATLAB-KL transform and use PCA Face Recognition small, the use of MATLAB
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Size: 2048 |
Author: 吴语 |
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Description: 图像处理领域最新的K-L变换后求主成分的程序。matlab文件。-image processing latest K-L transform PCA for the procedure. Matlab document.
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Size: 2048 |
Author: 宋争鸣 |
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Description: 基于K-L的人脸识别源代码和修改后的PCA进行人脸识别的Matlab源代码-based on K-L Face Recognition source code and modify the PCA for face recognition Matlab source code
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Size: 5120 |
Author: wh |
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Description: k-l和PCA算法在人脸识别中的具体实现(MATLAB)-kl and PCA Face Recognition Algorithm in the concrete realization of (MATLAB)
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Size: 4096 |
Author: xyl |
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Description: 人脸识别的matlab代码，本征脸（ eigenface ）方法是子空间人脸识别方法的典型代表。该方法基于一种部分的K-L 变换，或者称为主成分分析（ PCA ）-Matlab code for face recognition, eigenface (eigenface) sub-space method is a typical representative of face recognition methods. The method is based on a part of the KL transform, or known as principal component analysis (PCA)
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Size: 1024 |
Author: 李英豪 |
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Description: Malic是一个完整的Linux下的人脸识别系统源代码，它是SourceForge上的一个开源项目，使用Malib实现实时处理，CSU Face Identification Evaluation System进行人脸识别。算法包括：主成份分析(principle components analysis (PCA)),a.k.a eigenfaces算法,混合主成份分析,线性判别分析(PCA+LDA),图像差分分类器(IIDC),弹性图像匹配算法(EBGM)-Malic is a complete face recognition system under the Linux source code, it is a SourceForge open source project, using real-time Malib treatment, CSU Face Identification Evaluation System for Face Recognition. Algorithms include: Principal component analysis (principle components analysis (PCA)), aka eigenfaces algorithm, mixed-principal component analysis, linear discriminant analysis (PCA+ LDA), the image difference classifier (IIDC), a flexible image matching algorithm (EBGM)
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Size: 977920 |
Author: 严锐 |
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Description: 主成分分析方法（PCA），PCA算法的理论依据是K-L变换，通过一定的性能目标来寻找线性变换W，实现对高维数据的降维。-Principal component analysis (PCA), PCA algorithm is based on the theory of KL transform, through a certain performance targets to find the linear transformation W, the realization of high-dimensional data, dimensionality reduction.
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Size: 1024 |
Author: 李伟 |
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Description: 使用PCA抽取人脸灰度图片的前k个特征向量，计算人脸灰度图片的散布矩阵并且抽取其前k个作为主特征-using PCA to extracte gray picture face of the first k eigenvectors to calculate gray-scale image of the face spreading matrix, and taking its pre-k-as the main characteristics
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Size: 1024 |
Author: 许同彬 |
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Description: 用主成分分析法提取人脸图像特征的程序，算法理论依据是K-L变换-Principal Component Analysis with face image feature extraction process
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Size: 1024 |
Author: 牛险峰 |
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Description: FACE RECOGNITION USING K-L TRANSFORM on ORL face database
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Size: 3079168 |
Author: Mahesh Chandra |
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Description: K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the characteristics of data clustering data. The principal component analysis reduces the dimension, carries on the visualization operation to the data, reduces the dimension to the features.)
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Size: 33792 |
Author: 赵嘉慧
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Description: princa，用于pca主成分降维：计算第k主成份贡献率-累计贡献率-取累计贡献率大于等于90%的主成分(For PCA principal component dimensionality reduction: calculate the principal component contribution rate of K - the cumulative contribution rate - take the cumulative contribution rate greater than or equal to 90% of the principal component)
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Size: 2048 |
Author: 阁阁
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Description: Python实现PCA将数据转化成前K个主成分的伪码大致如下: ''' 减去平均数计算协方差矩阵计算协方差矩阵的特征值和特征向量将特征值从大到小排序保留最大的K个特征(Python PCA data into pseudo code before the K principal components are as follows: the characteristics of 'average minus the covariance matrix to calculate the covariance matrix eigenvalues and eigenvectors. The eigenvalues in descending order retain maximum K features)
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Size: 64512 |
Author: 193sd
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Description: 采用INP数据（145*145*200），该数据有16个类别， PCA进行数据降维，然后对降维数据采用kNN分类（k=1）。(Using INP data (145*145*200), the data has 16 categories, PCA carries out data reduction, and then uses kNN classification for dimensionality reduction data (k=1).)
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Size: 26624 |
Author: 纷纷666
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Description: 该算法主要包含PCA算法和K-Means聚类算法，用于SAR变化检测,包含数据图片。(The algorithm mainly includes PCA algorithm and K-means clustering algorithm for SAR change detection, including data images.)
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Size: 240640 |
Author: 墨辞 |
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Description: 基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。
经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set.
After PCA dimensionality reduction, the final KNN achieved a classification accuracy of over 97% in a 100-dimensional feature space.)
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Size: 11599872 |
Author: 曲小刀 |
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