搜索资源列表
DCC
- 基于典型相关性的线性鉴别分析，先用PCA对数据降维之后，再结合典型相性鉴别分析来得到转换矩阵-Based on a typical linear correlation analysis to identify, first PCA for data reduction, the combined analysis to identify the typical phase of the transformation matrix obtained
Genetic-algorithm-optimization
- 遗传算法的优化计算——————建模自变量降维-Genetic algorithm optimization-- modeling variable dimension reduction
PCAPSVM
- PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果-PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results
BubbleVisualization
- 数据的可视化，rar文件里面包含了可视化的效果，可以画出4-D的图形，颜色作为第四维，更好的呈现数据的空间结构，可以用PCA等做降维的可视化展现-Data visualization, rar file which contains a visual effect, you can draw a 4-D graphics, color as the fourth dimension, the better the spatial structure of the present data, yo
FaceRecog_src
- 程序结构 整个工程可以分为3个部分：算法、功能和应用。 算法部分 算法部分目前分为4个模块：人脸对齐、光照归一化、特征提取和选择、子空间降维，每个模块是一个项目，每个项目生成一个dll供功能部分显式调用。 功能部分 功能部分只有一个项目FaceMngr，该部分依赖于算法部分，实现人脸注册、训练、识别、导入/导出等具体功能。该项目生成一个dll供应用部分隐式调用。 应用部分 人脸识别Demo. 另外，工程中
DCT
- 先用小波变换进行降维后，再用DCT进行特征提取，然后用SVM分类识别，SVM需先安用libsvm工具箱，然后再可以运行，该程序包含ROL人脸库，一并上传。-First reduce the dimension of the wavelet transform, the then DCT feature extraction, and then use SVM classification, SVM must be safe to use libsvm toolbox, and then you
PCAKPCA
- PCA和KPCA程序，matlab实现，可用于模式识别时做降维或特征提取处理-PCA and KPCA program, matlab implementation, pattern recognition can be used to do when dealing with dimensionality reduction or feature extraction
lpp.rar
- 一种很重要的非监督降维方法，是流形学习算法Laplacian Eigenmap 的线性化方法，在人脸识别中效果非常好。,A very important method of unsupervised dimensionality reduction, manifold learning algorithm is Laplacian Eigenmap linearization method is very effective in face recognition.
weinat
- 基于DD算法的先验信噪比估计的维纳语音降噪完整程序，包括语音分帧，动态信噪比估计,噪声估计更新和帧的重构。很完整。-DD algorithm based on a priori signal to noise ratio is estimated that the integrity of the process noise reduction Wiener voice, including voice sub-frame, dynamic signal to noise ratio estim
PCALDA
- PCA+LDA经典人脸识别算法，先用PCA降维，再用LCA降维-PCA+ LDA classical face recognition algorithms, first PCA dimension reduction, reuse LCA dimension reduction
K-L
- 模式向量维数太大，无法进行模式识别,降维 -k-l
FastICA_25
- 独立分量分析的算法，用于分离出独立分量，用于图像降维，特征提取-Independent component analysis algorithms, used to separate out the independent component for the image dimensionality reduction, feature extraction
kpca081223
- 非线性降维方法KPCA 可以应用于高维数据的机器学习-KPCA nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
Dimension-reduction--toolbox
- 该工具箱中包含了多种降维算法。其中有传统的PCA和Local PCA算法，也有典型的流形学习算法，如Isomap、LLE、HLLE、Laplacian Eigenmaps 和 Local Tangent Space 。-The toolbox contains a variety of dimensionality reduction algorithms. In which the traditional PCA and Local PCA algorithms, there are the
PCA
- PCA,主成分分析，可应用于矩阵降维，人脸特征提取及人脸识别。-PCA, principal component analysis, can be applied to matrix reduction, facial feature extraction and face recognition.
sift-data-down
- sift 降维 有关详细资料，里面有着你想要的sift 降维资料-For more information sift dimension reduction, which has a dimension you want to sift data down
drtoolbox
- 降维工具箱，包含主元分析（PCA）,核主元分析（KPCA）等。-Dimensionality reduction kit, including principal component analysis (PCA), Kernel Principal Component Analysis (KPCA) and so on.
ltsa
- 一种用于非线性降维的流形学习算法，主要是先考虑用每一点处的局部切空间表示该点处的几何特征，然后都局部切空间进行排列。-A non-linear dimensionality reduction of manifold learning algorithm, mainly to consider every point of the partial cut Department said that the space of the geometric characteristics of poin
Isomap
- Isomap流形降维算法，包括了一个瑞士卷生成数据，dijk距离算法。-Dimensionality reduction algorithm Isomap manifold, including the formation of a Swiss roll data, dijk distance algorithm.
Rec_BaseOnCN
- 使用复杂网络提取图像边缘特征并进行识别的源代码，采用PCA_LDA算法对特征进行降维分类识别，识别效率很高。鲁棒性好-Extracted using image edge characteristics of complex networks and to identify the source code, using PCA_LDA algorithm to reduce the dimensions feature classification, identification with hi