主分量分析,用于高维数据降维或提取目标特征。程序精简，效率高.

【摘要】 目标的自动识别是最有价值的应用需求之一，但它同时也最具挑战性。过去几十年中该课题的研究己经取得了较大的进展，但计算机自动识别技术还远没有达到理想的实际应用需求。自动识别技术涉及到很多方面的研究，如图像的预处理，图像增强、图像分割、特征提取方法和分类器的设计等等，这其中特征提取方法的研究尤为关键。一方面，研究者对特征提取的理论作了较多的探索，力求得出一些针对特定目标的高精度、高效率的特征提取算法与方法。这其中包含PCA方法、Fisher鉴别分析方法，以及以核方法为代表的非线性特征提取方法等。另一方面，在实际应用中算法的效率也是非常重要的。本文的研究集中在特征提取方法，这其中涉及到线性与非线性特征提取方法。 本文将特征提取方法分为线性和非线性特征提取方法。原始信息经过线性映射得到的变换后信息称为线性特征，原始信息经过非线性映射得到的变化后的信息成为非线性特征。对应的映射成为线性特征提取方法和非线性特征提取方法。 主分量分析和Fisher线性鉴别准则是应用最广泛的特征提取算法。本文论述了2DPCA和2DFLD等传统特征提取方法，并发展了2DFLD特征提取方法，提出分块的2DFLD特征提取方法，分析表明，该方法是2DFLD方法的推广，在人脸识别研究中优于传统的2DFLD方法。 核方法是新近发展起来的一种非线性特征提取方法，它的理论基础来自于统计学习理论。本文详细讨论了核特征提取方法，并结合偏最小二乘理论(PLS)，提出了基于KPLS的特征融合方法。 本文以构造新的特征提取算法为主要的研究方向，并结合实际应用来验证算法的优劣，对于算法中部分参数的选择讨论不足，这将在以后的研究工作中予以关注。 还原 【Abstract】 ATR is one of the most significant requests, although it is also one of the most challenging tasks. During past several decades great progress has been made in research on this subject. However, it is far away from satisfactory requirements from real world. ATR involves many techniques, such as Image preprocessing; Image enhancing; Image Segmentation; Feature extraction; classifiers designing and so on. Feature extraction is crucial. On one hand, researchers attempt to work out algorithms and methods to some special targets with high right classification rate and good efficiency. Among them, Principal Component Analysis, Fisher’s Linear Discriminant, nonlinear algorithms mainly appearing as Kernel approaches, and so on. On the other hand, in real application efficiency is also an important indicator to assess one algorithm, because in many cases only algorithms with high efficiency can satisfy request of real task. This paper aims at designing feature extraction algorithms on face recognition, including linear feature extraction and nonlinear ones.Feature extraction approaches are divided into two groups in this paper, linear feature extraction and nonlinear feature extraction. The information after linear mapping is called linear features; the information after nonlinear mapping is called nonlinear features. The mappings are called linear feature extraction and nonlinear feature extraction correspondingly.Principal Component Analysis and Fisher’s Linear Discriminant are two methods widely used. This paper introduces feature extraction approaches, 2DPCA and 2DFLD, respectively. We develops the 2DFLD, and presents a new feature extraction approach called blocked FLD. 2DFLD is the special case of blocked FLD. the experimental results indicated that the recognition performance of blocked FLD is superior to that of 2DFLD.Kernel method is a powerful machine learning method developed recently. It builds on the statistical learning theory. Feature extraction based on kernel is discussed in detail. A feature fusion method combined with KPLS is proposed. 还原

为了对坐姿下的几种行为进行识别，在分析常有坐姿的基础上，提出了通过PCA对八种不同姿势进行分类识别的方法。结合背景帧信息通过背景轮廓消减法提取运动目标区域，利用肤色在YCbCr空间聚集在一片固定区域且在CbCr平面上投影为一个近似椭圆的特性，在运动目标区域提取肤色区域，并对检测出的肤色灰度图进行PCA运算，实现了姿势识别。实验结果表明，所提出的利用PCA进行姿势识别的方法正确率达到84.92%，能够准确地识别坐姿行为，并且对运动阴影、光线变化具有良好的鲁棒性。

基于ORL人脸库的2DPCA（二维主分量分析算法），2DPCA由理工大杨建老师提出

利用matab 编写的求解主分量分析的程序

无监督学习包括聚类分析，主分量分析，稀疏编码与学习

二维主分量分析(2DPCA) ，完整的2DPCA人脸识别，比较传统PCA具有更高的识别效率，识别成功率一不错