搜索资源列表
fisher
- 利用pca和LDA对人脸的图像先进行降维再辨别分析来实现人脸识别-Pca and LDA using the images of human faces to reduce the dimensions first and then discriminant analysis to achieve recognition
PCA
- 光谱空间的主成分分析 PCA ：旨在利用降维的思想，把多指标转化为少数几个综合标。-Principal component analysis of spectral space PCA: dimension reduction ideas to use to index into a few more comprehensive standard.
pca-vector
- pca特征向量提取 利用pca的方法获取特征植及特征向量 最后可以自己根据需要降维-pca pca feature vector extraction method using characteristics of plants and to obtain the final feature vector dimension reduction can be their own as needed
PR_paper
- 基于独立成分分析的人脸识别方法研究 基于流形学习的数据降维方法研究 人脸识别系统的设计与开发 基于主成分分析的人脸识别 特征提取算法的研究与改进。。。。。。 做人脸识别式一系列的参考资料 -Face recognition based on independent component analysis method based on manifold learning method of data reduction system design and developme
PCA
- LPA是主成分分析算法，用于特征提取和特征空间的降维-LPA code for feature
score
- 对获得的512维小波特征进行了PCA降维处理，将特征空间降至100维，并采用前面得出的最佳系数针对多项式核函数和高斯径向基核函数-On access to the 512-dimensional wavelet features to reduce the dimension of the PCA, will feature space to 100 dimensions, and using the best coefficients obtained for the previous pol
lda
- 对有类别监督的数据进行先行判别分析用于降维 数据为结构体类型，返回数据也是降维后的结构体类型。如(a.X a.y)a.X是带有类别的数据，a.y是类别信息-Synopsis: model = lda(data) model = lda(data,new_dim) Descr iption: This function is implementation of Linear Discriminant Analysis. The goal
PCA
- PCA算法主要用于提取高维特征，可以将图像这种高位数据降维成为低维向量-PCA algorithm
mddm(data)
- MDDM多标记降维,可以用来减少高维多标记数据的维度。-MDDM is a package for multi-label dimensionality reduction. It can be used to reduce the dimensionality of high-dimensional multi-label data.
KarhunenLoeve
- 数据降维算法 K_L变换是一种最优正交变换，以矢量信号X的协方差矩阵Ф的归一化正交特征矢量q所构成的正交矩阵Q，来对该矢量信号X做正交变换Y=QX，则称此变换为K-L变换（K-LT或KLT）。-Data reduction algorithm K_L optimal orthogonal transform is a transformation to vector signal covariance matrix Ф X normalized orthogonal feature vect
palmprint-recognition-
- 首先计算掌纹图像二维Gabo r小波变 换系数的幅值, 将其作为掌纹图像的原始特征 其次, 利用 2DPCA 实现原始特征的降维 然后, 利用PCA 与FLD 的融合算 法进行二次降维的同时提取出最有利于分类的鉴别特征 最后, 利用最近邻算法进行掌纹的分类识别。 -First calculate the two-dimensional Gabo r palmprint image wavelet coefficients of magnitude, its character
Face-recognition
- 本文针对人脸图像的特点，选取一组Gabor 小波核，并用这组Gabor 小波核对人脸图像进行Gabor 小波变换，提取人脸 图像的有效信息。在此基础上，用2DPCA 对Gabor 小波提取的 数据矩阵进行降维，最后用最近邻法对人脸进行分类。-In this paper, the characteristics of face images, select a set of Gabor wavelet kernel, and check with this set of Gabor wav
treelet-testcode_April08
- 一维无序随机信号的小树变换处理，小树变换即treelet，是一种有效的高维的，无序的，杂乱的数据进行降维，特征提取的方法-One-dimensional disorder random signal processing, the saplings transform treelet tree transform namely, is a kind of effective high-dimension, disorder, messy data dimension reduction, fea
laplacian_eigen
- 拉普拉斯特征映射，采用热核构造权重，是一种基于流行学习的非线性降维技术，可用于图像分割提高聚类的性能-Laplacian Eigenmap is a kind of nonlinear dimensionality reduction technique which based on manifold study, it choose the weights W using the heat kernel and it can be used for image segmentation to
isomap
- 等距映射，一种用于非线性降维的全球性的几何框架，适用于学习内部平坦的低维流形-Isomap is a global geometric framework for nonlinear dimensionality reduction,it suitable for learning low dimensional manifold which inside is flat
PCA
- 主成分分析，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，主要用于数据降维-PCA，a kind of statistical analysis method which master the main self-contradict of things, it can resolve the main factors from multiple things reveal the nature of things, simplify
ensembles_pca_svm_new10v
- pca做特征降维，然后进行特征空间随机分割构造多个svm分类器，并行处理，对样本进行分类，基于特征空间的svm多分类器-using pca reduce feature dimension,split feature space and then randomly divided over svm classifier construction, parallel processing, the samples were classified, based on multi-feature sp
gabor-pca
- 本程序是先用gabor小波变换对人脸图像处理，然后在用pca进行降维，最后用svm分类器进行多分类分类识别，包扩完整的orl人脸库，需注意的是，svm工具箱是用的libsvm工具箱，运行前先配置好libsvm。版本号：libsvm-mat-2[1].89-3[FarutoUltimate3.0]-This procedure is to use the human face gabor wavelet transform image processing, and then to reduce
PCA
- 该程序主要是用于模式识别对样本进行降维处理，该程序是通过PCA的方法实现降维-The program is mainly used for pattern recognition to reduce the dimension of the samples, the program is achieved through the method of PCA dimensionality reduction
IMAGE-matching
- 用MATLAB来实现简单图像特征点提取和匹配-Using MATLAB to implement a simple image feature extraction and matching