搜索资源列表
pca
- 经典主成分分析法，实现高光谱图像的降维处理 -Classical principal component analysis, to achieve high spectral image dimensionality reduction
laplacian_eigen
- 拉普拉斯算法，对高光谱图像进行降维处理，比较实用，多层分解方法-Laplace algorithm to reduce the dimension of hyperspectral image processing, more practical, multi-layer decomposition method
kernel_pca
- 核主成分分析法，用于高光谱图像的降维处理，效果较好-Kernel principal component analysis for hyperspectral image dimensionality reduction, better
drtoolbox-use
- drtoolbox降维工具箱程序包以及使用的范例 LLE，LTSA，ISOMAP，等- examples of drtoolbox use
Principal-component-analysis-code
- 在高光谱图像处理和分析中,主成分分析方法是使用最广泛的线性降维 方法之一,它概念简单,实现算法高效"在信号处理中,所谓的Karl血nen.b姆ve 变换实际上就是主成分分析 -In hyperspectral image processing and analysis, principal component analysis is the most widely used linear dimensionality reduction methods, it is conceptu
OLDA
- OLDA算法,可用于样本书和类别数较少的数据降维-OLDA algorithm
PCA_LDA_LPP_Tensor
- PCA特征提取。LDA降维的人脸识别代码。可用作为初学者使用。-PCA feature extraction. Face recognition code LDA dimensionality reduction. Available for beginners to use.
Hilbert_Curve
- 将地理位置相邻的二维坐标，处理为队列的一维编号，实现空间降维。-The location adjacent to the two-dimensional coordinates, treated as one-dimensional queue number, spatial dimensionality reduction.
Modeling-of-variable-
- 遗传算法的优化计算，建模自变量降维，matlab经典算法-Genetic Algorithm optimization, modeling arguments dimensionality reduction, matlab classical algorithm
mobanshenchen
- 直方图压缩模板，输入的一定bins的直方图，输出48bins的直方图，可用于图像识别，在对图像提取直方图特征后进行降维操作-Compression template histogram, histogram bins certain input, output 48bins histogram, can be used for image recognition, the histogram of the image feature extraction operation after the
pcaagabor
- pca特征降维，gabor小波变换，人脸识别的matlab程序-pca feature dimension reduction, gabor wavelet transform, face recognition matlab program
pcaneartemplet
- pca主成分分析降维，并应用与标准模板的数字识别-pca PCA dimensionality reduction, and apply digital identification with the standard template
2DPCA
- 2DPCA,即二维主成分分析，相对于传统的PCA（主成分分析），2DPCA在对二维图像进行降维时不需要转成一维（向量）-2DPCA, ie two-dimensional principal component analysis, as opposed to the traditional PCA (Principal Component Analysis), 2DPCA in dimensionality reduction of two-dimensional images into one
drtoolbox
- 降维工具箱，包括LTSA,PCA等，有助于这方面的学习，有意的朋友可以来下载-Dimensionality reduction toolbox, including LTSA, PCA, etc., help in this area of study, interested friends can be downloaded
PCA1
- pca算法，用于数据降维，注释非常详细清晰，-PCA algorithm, for data dimensionality reduction, clear and very detailed notes,
KNN
- 基于PCA降维的KNN，最近邻分类matlab实现。-PCA dimensionality reduction based the KNN, the nearest neighbor classification matlab.
PLS
- pls算法集合，包含表格数据读取，过程变量降维、潜变量的投影方式-pls、pca algorithm
PCA
- 实现图像的降维处理,可以缩短大量的计算时间。-Dimensionality reduction of image processing, a lot of computation time can be shortened.
MFA
- 自己写的MFA降维算法。此算法中先进行pCA处理原始数据，然后对处理后数据运用MFA，可用于人脸识别及其它分类问题。很好用。-Write your own MFA dimensionality reduction algorithm. This algorithm first performed pCA processing raw data, processed data and then use MFA, can be used for face recognition and other
PCA
- 针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的