搜索资源列表
TE_PCA
- PCA:主元分析。进行降维，实现TE化工过程特征提取。-PCA: principal component analysis. Dimensionality reduction, feature extraction chemical process to achieve TE.
LDA
- LDA:线性判别分析方法。用于实现线性数据降维。采用K近邻分类器对数据进行分类-LDA: linear discriminant analysis method. Used to achieve linear data dimensionality reduction. Using K-nearest neighbor classifier for data classification
LMDA
- LMDA:一种改进的线性判别方法。用于实现线性数据的降维，进行特征提取。-LMDA: An improved linear discriminant analysis. Used to achieve linear data dimensionality reduction, feature extraction.
KFDA
- KFDA:核fisher用于分线性数据的降维。对数据进行特征提取。-KFDA: kernel fisher for sub-linear data dimensionality reduction. Data for feature extraction.
drtoolbox
- 国外的牛人写的软件，数据降维工具箱，包含了几十种常用的流形学习方法的源码，自带图形界面。-A foreign master write this dimension reduction toolbox,there s dozens of code of common manifold learning methods.
LLE
- LLE流行算法可以在保持原来数据拓扑结构的条件下实现数据的非线性降维。-LLE popular algorithms can maintain the original data in the topology of the data without becoming realized under conditions of nonlinear dimensionality reduction.
RSC
- RSC实现人脸识别，使用PCM降维并且采用gabor滤波器来提高识别率-RSC face recognition, using the PCM dimensionality reduction and the use of gabor filter to improve the recognition rate
fastPCA
- 快速PCA算法，用于对大规模数据进行PCA降维，节省时间-fastPCA for large scale data which can save proceeding timE
pca
- 可用于高光谱图像处理的自己编的PCA函数,Matlab版的.主要作用是降维.-the pca function is used to hyperspectral image processing.
llde_cmb
- 人脸检测一直是人们在研究的问题，流形学习用于人脸检测中的特征提取，用PCA与constructM进行降维，KNN分类器用于分类。取得非常好的效果。-Face detection has been the problem of people in the study, manifold learning for face detection feature extraction using PCA and constructM dimension reduction, KNN classifier
1
- 本程序实现LPP降维功能，用于多模态故障诊断中-This procedure realize the LPP dimension reduction function, used in multimodal fault diagnosis
Full2012
- 1. 本研究利用 PCA 对可见-近红外（450～1 000 nm）、可见光（450～780 nm）和近红外（780～1 000 nm）光谱区域的苹果高光谱图像数据进行降维，获得 PC 图像，通过对 PC 图像进行分析，确定可用于分割损伤和正常区域的有效光谱区域，对比分析几个光谱区域的 PCA 的效果。-but currently no practical system for detecting blood spots and dirt stains exists. In order to
mani-isomap
- 流行学习isomap（有标注，便于更好理解等距映射方法在高维数据降维过程的实现）-Popular learning isomap (have annotations, facilitate better understanding isometric mapping method in the implementation of the high-dimensional data dimension reduction)
LDE-Algorithm
- 局部线性判别嵌入算法，用于实现高维数据的特征提取与低维嵌入，可以很好地实现数据的降维。-Local linear discriminant embedding algorithm, used to implement the feature extraction and the low dimensional embedding of high-dimensional data, can well realize data dimension reduction.
SDEmatlab
- 基于监督学习的一种非线性数据降维方法，可以很好地将低维特征从其所在的高维流形中提取出来，用于数据分类。-A nonlinear data dimension reduction based on supervised learning method, is a good way to the lower dimensional feature extracted from its place of higher dimensional manifold, used for data classi
zpav
- ZPAV 是以小波，降维，剪切零树，运动估计，算术编码等算法为理论基础的音视频编解码协议，具有压缩率高，比特率低而稳定， 应用领域广，发展潜力大，延拓性好，复杂度适中，易于集成电路实现等特点，是理想的信源编解码协议。-ZPAV (H265) is audio-visual codec protocol, very different from H264/MPEG4, ZPAV (H265) basic algorithm is wavelets, SPIHT, BSW, MMW, .....
chapter27
- 遗传算法优化计算-建模自变量降维 缩短了建模时间，提高建模精度-Genetic algorithm optimization- modeling arguments dimensionality reduction reduces modeling time and improve modeling accuracy
lfda
- 从高维信号提取特征并同时降维到低维信号。为图像表示、图像分类、模式识别等进行特征提取。-local Fisher Discriminant Analysis
eg27-zibianliangjiangwei
- 《MATLAB神经网络30个案例分析》中的第27个例子，案例27 遗传算法的优化计算——建模自变量降维。希望对大家有一定的帮助！-The MATLAB neural network analysis of 30 cases of 27 example, 27 cases of genetic algorithm optimization, modeling the independent variable dimension reduction. Hope to have certain hel
scd
- 逐步共线性诊断在成分分析方法降维精确性中的应用-Gradually the collinearity diagnosis analysis method application of reduced- dimension accuracy in composition