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一个 PCA 算法的matlab程序
- 主成分分析（PCA)算法是用于简化数据的一种技术,对于某些复杂数据就可应用主成分分析法对其进行简化。-principal component analysis (PCA) algorithm is used to simplify the technology of data, For some complex data can be applied Principal Component Analysis streamline its.
pca++
- 人脸识别方面PCA算法的matlab实现-face recognition aspects of the PCA algorithm to achieve Matlab
pca-matlab-code
- pca主成分分析算法matlab源码，利用matlab实现pca算法。-pca principal component analysis algorithm Matlab source, using Matlab achieve pca algorithm.
PCA-code
- PCA原代码，可以移植到别的pca算法中使用，具有较强的可移植性。-PCA original code can be transferred to other pca algorithm used, with a strong portability.
PCA-SIFT
- 一个基于matlab语言的编程，可以实现对人脸的识别算法-A matlab-based programming language can be achieved on the human face recognition algorithm
sift-algorithm-pcasift-asift-csift
- 一些有关SIFT衍生算法的资料，包括ASIFT, CSIFT, PCA-SIFT, SURF算法，中英文都有，很有价值-Some of the SIFT algorithm is derived, including ASIFT, CSIFT, PCA-SIFT, SURF algorithm, in each category, great value
pca
- 采用C++仿真实现利用PCA算法进行人脸特征提取-Simulation using C++ using PCA algorithm for face feature extraction
PCA
- 完全用C++实现的PCA算法，可以用于实际工作和学习中-Entirely in C++ implementation of the PCA algorithm can be used for practical work and learning
PCA-SIFT
- PCA-SIFT算法的实现，PCA-SIFT是对SIFT算法的改进，用PCA替代SIFT算法中的第四步，提高了效率，且准确率更高-PCA-SIFT algorithm implementation, PCA-SIFT SIFT algorithm is an improved SIFT algorithm with PCA instead of the fourth step, improve efficiency, and higher accuracy
PCA
- PCA人脸识别算法，识别率达到99 ，采用小波变换的方法及主成分分析法。-PCA face recognition algorithm, the recognition rate up to 99 , using wavelet transform methods and principal component analysis.
pca-sift
- pca-sift算法的实现，采用vc2008开发环境，进行开发，独立运行-pca-sift algorithm, using vc2008 development environment to develop, operate independently
FaceRec
- This example face recognition with opencv using PCA algorithm
Robust PCA
- 用于拉格朗日函数分解运算的PCA算法，MATLAB程序实现(PCA algorithm for decomposition operation of Lagrange function)
pca-master (1)
- 此源代码使用Eigen C ++库在C ++中实现了PCA算法。 运行代码所需的组件： - 安装Visual Studio 2012（Express Edition工作正常） - Windows操作系统（Windows 7或更高版本） 如何运行源代码： - 打开文件。\ pca \ PrincipalComponentAnalysis \ PrincipalComponentAnalysis \ Program.cpp - 在行和列变量中设置矩阵的尺寸。 - 在m变量中设置
PCA
- 以一个实际例程为例，实现了PCA算法的全过程。附带有数据集。方便读者调试。可以直接运行。(Taking an actual routine as an example, the whole process of PCA algorithm is realized. Comes with a data set. Convenient reader debugging. Can run directly.)
改过的pca在matlab中的实现
- 简单的pca算法在matlab中的实现。附带解释。(Implementation of a simple PCA algorithm in MATLAB.)
pca
- PCA算法可在MATLAB中配合人脸数据库实现人脸识别(PCA algorithm can be used to realize face recognition algorithm.)
pca-人脸识别
- 运用PCA算法，可以通过摄像头实时获取人脸，进行人脸识别。(By using PCA algorithm, we can get face in real time by camera and face recognition.)
PCA
- 不用自带函数，而是直接编程实现PCA算法。然后用PCA实现将数据从三维降到二维。(PCA algorithm is realized by direct programming instead of self-contained functions.Then PCA is used to reduce the data from three-dimensional to two-dimensional.)
PCA-K
- 该算法主要包含PCA算法和K-Means聚类算法，用于SAR变化检测,包含数据图片。(The algorithm mainly includes PCA algorithm and K-means clustering algorithm for SAR change detection, including data images.)