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Description: 主成分分析程序，适合做向量场的正交分解
-principal component analysis procedures, suitable for the vector field orthogonal decomposition
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Author: hahaha |
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Description: 介绍主成分分析的电子书籍，个人认为比较好-PCA introduce e-books, personal think is better
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Size: 92160 |
Author: 胡兵 |
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Description: 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
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Size: 249856 |
Author: 崔伟 |
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Description: 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
» Sensible PCA (E-M algorithm for probabilistic PCA)
– Mixtures of Probabilistic Principal Component
Analysers-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
» Sensible PCA (E-M algorithm for probabilistic PCA)
– Mixtures of Probabilistic Principal Component
Analysers
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Size: 263168 |
Author: Tatyana |
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Description: 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 influence of the
eigenvectors which relate to the changes of the iUumination on abstract features，a modified PCA ( A)
algorithm is propo sed．The method is based on the idea of reducing the influence of the eigenvectors associated
with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation．
Th e Yale face database and Yale face database B are used to verify the method．The simulation results show
that，f0r front face and even under the condition of limited variation in the facial po ses the proposed method
results in better perform ance than the conventional PCA and linear discriminant analysis(LDA)approaches．and
the computational cost remains the same as that ofthe PCA，and much less than that ofthe LDA．
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Size: 205824 |
Author: 费富里 |
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Description: PCA:Principal Components Analysis
It computes Principal Component Analysis, i.e., the linear transform which makes data uncorrelated and minize the reconstruction error.
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Size: 1024 |
Author: Lara |
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Description: 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
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Size: 947200 |
Author: xiaobingee |
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Description: 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,
users can utilize the class for principal component analysis(PCA) and fisher
discriminant analysis(FDA). It can also be used for some elementary matrix and
vector computations.
Usage
=====
It s a C++ program for symmetric matrix diagonalization, inversion and principal
component anlaysis(PCA).
To use it, you need to define an instance of CMatrix class, initialize matrix,
call the public funtions, and finally, free the matrix. For example, for PCA,
CMarix theMat // define CMatrix instance
float** C // define n*n matrix
C = theMat.allocMat( n )
Calculate the matrix (e.g., covariance matrix from data)
float*phi,*lambda // eigenvectors and eigenvalues
int vecNum // number of eigenvectors (<=n)
phi = new float [n*vecNum]
lambda = new float [vecNum]
the
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Author: 朱 |
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Description: 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(Distinctiveness)好，信息量丰富，适用于在海量特征数据库中进行快速、准确的匹配[23]。
c) 多量性，即使少数的几个物体也可以产生大量SIFT特征向量。
d) 高速性，经优化的SIFT匹配算法甚至可以达到实时的要求。
e) 可扩展性，可以很方便的与其他形式的特征向量进行联合。
4 SIFT算法步骤：
1） 检测尺度空间极值点
2） 精确定位极值点
3） 为每个关键点指定方向参数
4） 关键点描述子的生成
本包内容为sift算法matlab源码-1 SIFT course of development
SIFT algorithm by DGLowe in 1999, the perfect summary of 2004. Later Y.Ke its description of the sub-part of the histogram with PCA instead of its improvement.
2 the SIFT main idea
The SIFT algorithm is an algorithm to extract local features in scale space to find the extreme point of the extraction location, scale, rotation invariant.
3 the main features of the SIFT algorithm:
a) SIFT feature is the local characteristics of the image, zoom, rotate, scale, brightness change to maintain invariance, the perspective changes, affine transformation, the noise also maintain a certain degree of stability.
b) unique (Distinctiveness), informative, and mass characteristics database for fast, accurate matching [23].
c) large amounts, even if a handful of objects can also produce a large number of SIFT feature vectors.
d) high-speed and optimized SIFT matching algorithm can even achieve real-time requirements.
e) The scalability can be very convenient fe
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Author: 李青彦 |
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Description: 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
performance in biometric recognition system. This multimodal biometrics system introduces recognition of person from two
things i.e. face & palm print. Principal Component Analysis (PCA) algorithm is used for reduction of dimension & extraction of
features in terms of eigenvalues & eigenvectors. Feature level fusion technique used to fuse the results of face & palm prints and
then gives the output as per neural network classifier which gives the correct information about genuine or imposter identity.
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Author: atish |
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Description: 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 final/reduced Dataset should contain can be chosen by the user.
It contains a illustration dataset of faces (face.mat)(please see Read-me file) to show the usage.
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Author: 张三 |
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Description: 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
biometric data should be regarded as one class in the input space,
and his different biometric data can form different Gaussians
distributions, i.e., different subclasses. Hence, we propose a novel
multi-modal feature extraction and recognition approach based
on subclass discriminant analysis (SDA). Specifically, one
person’s different bio-data are treated as different subclasses of
one class, and a transformed space is calculated, where the
difference among subclasses belonging to different persons is
maximized, and the difference within each subclass is minimized.
Then, the obtained multi-modal features are used for
classification. Two solutions are presented to overcome the
singularity problem encountered in calculation, which are using
PCA preprocessing,-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
biometric data should be regarded as one class in the input space,
and his different biometric data can form different Gaussians
distributions, i.e., different subclasses. Hence, we propose a novel
multi-modal feature extraction and recognition approach based
on subclass discriminant analysis (SDA). Specifically, one
person’s different bio-data are treated as different subclasses of
one class, and a transformed space is calculated, where the
difference among subclasses belonging to different persons is
maximized, and the difference within each subclass is minimized.
Then, the obtained multi-modal features are used for
classification. Two solutions are presented to overcome the
singularity problem encountered in calculation, which are using
PCA preprocessing,
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Size: 588800 |
Author: avinash trivedi |
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Description: 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_GUI function, I put E disk, note the path, the current recognition rate in general, also welcomed the exchange correction 1127851044@qq.com, thank you.
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Size: 20869120 |
Author: 李猛 |
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Description: 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.)
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Author: SONAH~
|
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Description: 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, a low-dimensional face space (pca) is created using training images. This is obtained by training principal component analysis (PCA) of image set and principal component analysis of image (i.e. eigenvectors with larger eigenvalues).
As a result, all the test images are represented by the selected principal components, the Euclidean distance between the projected image and all the projected train images is calculated, and the minimum value is selected to find the training image most similar to the test image.)
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Author: bbqQq |
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