搜索资源列表
K-L_face
- 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
PCA-MATLAB
- k-l和PCA算法在人脸识别中的具体实现(MATLAB)
PCA
- 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。
stprtool.rar
- 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines,
PCA.rar
- 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
chengxu
- 这是基于PCA的人脸识别，用MATLAB编写，包含了K-L变换，奇异值分解等方法，且采用了最小距离分类器-This is based on the PCA face recognition, using MATLAB to prepare, including the KL transform, singular value decomposition and other methods, and the use of the minimum distance classifier
stprtool15aug08
- 统计模式识别算法包，包括线性分类算法，SVM，PCA，LDA，EM，k-means分类等多种常用的模式识别算法。-Statistical pattern recognition algorithm package, including a linear classification algorithm, SVM, PCA, LDA, EM, k-means classification and many other commonly used pattern recognition algori
K-L
- K-L又叫PCA，是人脸识别最基础的一个算法-KL also known as PCA, are the most basic of a Face Recognition Algorithm
malic
- 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)等等 Malic is realtime face recognition system
keigen
- 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
knnPcaWithGA
- In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification is made by K-In this code I have used GA in supervised PCA to find the best coeficients for overall covariance. the classification i
moja_PVA_normiran_KNN_koncan
- PCA with K-nn classifier(for pictures)
2D-PCA
- FACE RECOGNITION USING K-L TRANSFORM on ORL face database
K-Means PCA降维
- 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 character
pca
- 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 tha
PCA
- 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 covari
PCA
- 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).)
基于PCA的人脸识别
- 主成分分析法(principal conponent analysis, PCA)也叫Hotelling变换或特征脸法，是基于 K-L变换基础上研发得到的。该方法的核心是能够降低图像空间的维度，具体做法是将原始的数据通过某种线性变换从高维度空间转变到低维度空间中，这些数据彼此不相关，根据贡献率选取最大的前一部分，使原数据具有最大的变化量，对后面的图像也向这个空间投影，然后比较它们之间的距离来确定类别关系。PCA方法的缺点是对光照问题比较敏感。
PCA-K
- 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.)
PCA+mnist
- 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 dime