基于PCA与蚁群算法的机械故障聚类诊断方法.pdf

主成分分析(Principal components analysis)是最常用的降维方法 算法步骤： （1）对所有样本进行中心化操作 （2）计算样本的协方差矩阵 （3）对协方差矩阵做特征值分解 （4）取最大的d个特征值对应的特征向量，构造投影矩阵

主成分分析(PCA)python实现(含数据集)，结构清晰，适合初学者

基于PCA的非局部聚类稀疏表示图像重建方法.pdf

Python数据分析与机器学习-PCA主成分分析 Python数据分析与机器学习-PCA主成分分析

svd算法matlab代码 PCA Matlab PCA algorithm sample code using SVD

用于异常值检测，使用的方法为RPCA

实现精确高效的 L1-PCA 求解器的 MATLAB 函数集合。 L1-PCA 是 PCA/SVD 的抗异常值替代方案。 该工具箱为数据矩阵 X（D × N）的 L1-PCA（K 分量）提供函数； K<rank(X)<=min(D,N)。 作者：Panos Markopoulos 教授 (pxmeee@rit.edu) **** 请阅读每个代码的参考文章，以便您了解其复杂性和性能细节。 请注意，l1pca_EX 和 l1pca 是具有较高理论意义的精确代码，但在分析矩阵的大小方面也具有较高的计算成本（根据 L1-PCA 的组合性质）。 l1pca_BF 是中/大型问题的实用求解器。 **** 函数 l1pca(X,K) 使用多项式时间算法计算矩阵 X 的 K 个精确 L1-PC： PP Markopoulos、GN Karystinos 和 DA Pados，“L1 子空间信号处理

About this book Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques it continues to be the subject of much research, ranging from new model- based approaches to algorithmic ideas from neural networks. It is extremely versatile with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years. Written for: Researchers, graduate students

内有两个不同数据集的测试，测试集可选，训练方法可选，优化方法可选（内点法，smo）； 非对齐人脸准确率可达到80左右，对齐人脸可达到96左右； 使用支持向量机和PCA降维方法实现；