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e-AN115
- PCA仿真软串口使用说明 pdf文件,配合程序的说明-PCA use simulation software serial pdf file
probabalistic_PCA
- Probabilistic Principal Component Analysis – Latent variable models – Probabilistic PCA • Formulation of PCA model • Maximum likelihood estimation – Closed form solution – EM algorithm » EM Algorithms for regular PCA
ImprovedPCAFaceRecognitionAlgorithm
- 摘要：主成分分析（PCA）的人脸识别算法，以减少的特征向量是涉及到对抽象的特点，改进了主成分分析（一）iUumination算法的变化影响酶原sed.The方法是基于上减低与正常化其相应的标准差的特征向量元素相关联的大特征值的特征向量的影响力的想法。耶鲁大学和耶鲁大学面临的数据库面对数据库B是用来验证-Abstract：In principal component analysis(PCA)algorithms for face recognition，to reduce the influen
pca
- PCA:Principal Components Analysis It computes Principal Component Analysis, i.e., the linear transform which makes data uncorrelated and minize the reconstruction error.
PCA_tuxiangfenlei
- 基于扩展PCA的图像分类技术，电子书的，选择-Image Classification Based on Extended PCA technology, e-books, and select the next lower
CMatrix
- 对称矩阵相关元算，主成分分析(PCA)， fisher discriminant analysis(FDA).，-Introduction ============ This is a class for symmetric matrix related computations. It can be used for symmetric matrix diagonalization and inversion. If given the covariance matrix,
sift
- 1 SIFT 发展历程 SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 3 SIFT算法的主要特点： a) SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。 b) 独特性(Distinctive
FINAL
- Nowadays security becomes a most important issue regarding a spoof attack. So, multimodal biometrics technology has attracted substantial interest for its highest user acceptance, high security, high accuracy, low spoof attack and high recognition
pca
- 本文实现了众所周知的PCA算法。它返回一个减少号尺寸/特征数据集。折减系数，即多少特征最终/减少集应该包含可由用户选择。 它包含一个面说明数据集（脸。垫）（请参阅自述文件）如何使用。-this implements the well known PCA algorithm. It returns a Dataset with reduced no. of dimensions/features. The reduction factor i.e how many features the f
06094337
- When extracting discriminative features multimodal data, current methods rarely concern the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s overall biomet
All-Files
- 用MATLAB实现基于主成分分析（PCA）和支持向量机（SVM）的人脸识别系统，打开运行FR_GUI函数即可，我放在E盘中的，注意一下路径，当前识别率一般，也欢迎交流指正1127851044@qq.com，谢谢。-Using MATLAB analysis (PCA) based on principal component analysis and support vector machine (SVM) face recognition system to open the run FR_G
gpldecha-e-pca-d542a9b
- PCA是一种非线性降维方法特别适合于概率分布，得到了指数族PCA的POMDPs压缩。(Matlab implementation of E-PCA which is a non-linear dimensionality reduction method particularly suited for probability distributions, see the paper Exponential Family PCA for Belief Compression in POMDPs.)
matlab表情识别
- Matlab表情识别，特征脸[1 ]作为面部表情分类的方法。首先，利用训练图像创建低维人脸空间（pca）。这是通过训练图像集主成分分析（PCA）及图片主成分分析（即具有较大特征值的特征向量）获得的。 结果，所有的测试图像以所选择的主成分表示，计算投影图像与所有投影列车图像的欧几里得距离，选择最小值以找出与试验图像最相似的训练图像。(The feature face [1] is used as a facial expression classification method. Firstly,