搜索资源列表
PCA
- 主成分分析的代码，降维的工具，特征提取降维的工具-PCA code
pca
- 主成分分析代码，主要是做已经讲图片转换为像素的TXT文档的降维处理，提取主成分-Principal component analysis code, mainly to do is talk TXT pixel image is converted to reduce the dimension of the document, extracting the principal component
pca
- 主成分分析 pca函数代码，用于数据的降维、压缩、去噪、主成分分析等方面-principal component analysis
GaitRecognition
- 里面包含了步态识别的整一套流程的代码实现，包括从视频流里面提取图片帧，背景建模，提取运动目标（运动检测），形态学处理（膨胀与腐蚀，连通性检测），归一化大小，步态能量图的构建，主成分分析（PCA）降维,线性判别分析（LDA）分类等等功能的代码实现。均通过测试。-Contains the gait recognition of the whole process of a set of code, including the extraction and picture frames from th
PCA
- 主成分分析JAVA实现代码，可以实现数据的降维工作。-Principal component analysis JAVA code, can reduce the dimension of work data.
PCA
- 主成分分析的代码，利用PCA进行降维，是一个很清晰的代码-PCA analysis
bh_tsne.tar
- 本代码实现TSNE降维，首先利用PCA进行主成分分析，选取何时的特征再降维-dimension reduction for TSNE，we first use Principal Component Analysis to dimension reduction.
r_25kq4s
- 刚刚出炉的matlab代码，实现离散小波变换，进行主成分分析进行数据降维，用于模式识别，如temp识别，掌纹，表情，指纹，temp1等识别。 -Just-released matlab code to achieve the discrete wavelet transform, principal component analysis for data dimensionality reduction for pattern recognition, such as temp recogn
pca
- 主成分分析法 实现图像数据的降维 matlab代码编写-PRINCIPAL COMPONENT ANALYSIS OF IMAGE DATA
kPCA_v3.1 (1)
- KPCA用于数据特征的降维，先通过核方法，将样本映射到高维空间，再进行主成分分析过程。(KPCA is used to reduce the dimension of data, first through the kernel method, the sample is mapped to high-dimensional space, and then the principal component analysis process.)
pca
- 该脚本可以用于降维或者特征选择，名字为主成分分析。(Dimensionality reduction)
pca_fld
- 这是主成分分析pca和fld的代码，主要实现特征降维。(This is the principal component analysis of pca and fld code, the main feature reduction dimension.)
PCA
- 实现图片处理的传统pca降维，需要自己改文件路径(To reduce the dimension of traditional PCA in image processing, we need to change the file path by ourselves)
LLTSA降维
- 这个是KPCA核主成分分析的代码，好用，里面也带有范例(This is the KPCA kernel principal component analysis code, which is easy to use and also contains examples.)
主成分分析降维代码（直接调用版）
- 主成分分析降维代码，完整版，可以直接放进matlab运行。(Principal component analysis dimension reduction code, complete version, can be directly put into Matlab to run.)