Description: 此程序为用于主成分分析的matlab程序，可以输出贡献率及画出二维散点图-this procedure for the Principal Component Analysis Matlab procedures can export contribution rate and the two-dimensional plot paint Platform: |
Size: 4150 |
Author:林子 |
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Description: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换 和反变换 。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training. Platform: |
Size: 1024 |
Author:浇洒距离 |
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Description: 此程序为用于主成分分析的matlab程序，可以输出贡献率及画出二维散点图-this procedure for the Principal Component Analysis Matlab procedures can export contribution rate and the two-dimensional plot paint Platform: |
Size: 4096 |
Author:林子 |
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Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher. Platform: |
Size: 2048 |
Author:小弟 |
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Description: 基于二维PCA对人脸进行识别，对图像有很好的降维作用，且识别率比pca好-PCA based on two-dimensional human face recognition, the image is very good landing peacekeeping role, and the recognition rate better than pca Platform: |
Size: 2048 |
Author:周荧荧 |
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Description: 二维PCA人脸识别源码，针对ORL人脸库，识别率98％，matlab语言编程-Two-dimensional PCA face recognition source code for ORL face database, the recognition rate of 98, matlab language programming Platform: |
Size: 1024 |
Author:wct |
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Description: 该文介绍了二维主元分析在人脸识别中的应用研究。-This paper introduces the two-dimensional PCA in face recognition in applied research. Platform: |
Size: 559104 |
Author:Jacky |
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Description: Two-Dimensional PCA:
A New Approach to Appearance-Based
Face Representation and Recognition Platform: |
Size: 1379328 |
Author:cfdragon |
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Description: 二维的LDA+PCA人脸识别matlab程序，都可以直接使用。-Two-dimensional LDA+ PCA face recognition matlab procedures can be used directly. Platform: |
Size: 2808832 |
Author:风铃 |
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Description: 对UCI数据集之一进行PCA特征抽取实验,给出在二维PCA特征空间的数据散点图。-UCI data sets on one of PCA feature extraction experiments are given in the two-dimensional PCA feature space of the data scatter. Platform: |
Size: 1024 |
Author:chris |
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Description: 元胞自动机的虚拟程序
开发工具：Microsoft Visual Basic 6.0
运行平台：Windows OS
(VB编译，代码未优化,运行速度较慢)
词语解释：
CA，cellular automata，元胞自动机，或称细胞自动机。
模式(Pattern)，多个细胞构成的一个状态组合。
模型：
维度，二维四方网格
细胞，两种状态（活、死）；一个方格代表一个细胞。
邻居，Moore型(上、下、左、右、左上、右上、左下、右下)
规则，1)一个活着的细胞如果保持活的状态，那么它必需具有2至3个活的邻居，否则它就变成死的状态。2)一个死的细胞如果变成活的，那么它必需恰好有3个活的邻居。
程序功能：
1. 方格水平数量和垂直数量自由设置(最少为2，即2*2的网格)
2. 随机初始化细胞（细胞为活的概率可设置）
3. CA状态的演化，可自动逐步也可手动逐步。
4. 两种显示模式（用于研究可能的规律）
5. 可对CA状态进行编辑（操作类似简单的绘图）。
6. 模式。可以选择局部网格为模式；模式可以被旋转和翻转操作；模式可以被保存到文件和插入到网格。
7. 可以保存当前CA的状态到文件，也可载入已保存的CA状态。-Cellular Automata virtual procedure
Development tools: Microsoft Visual Basic 6.0
Platform: Windows OS
(VB compiler, the code is not optimized to run more slowly)
Words to explain:
CA, cellular automata, cellular automaton, or cellular automata.
Pattern (Pattern), consisting of a number of cells in a state of combination.
Model:
Dimension, two-dimensional grid Quartet
Cells, two states (living and dead) a grid on behalf of a cell.
Neighbors, Moore-type (upper and lower, left, right, left, right, left, bottom right)
Rules, 1) If a living cell to maintain a state of living, then it must have 2-3 living neighbors, otherwise it becomes a state of death. 2) If a dead cell becomes live, then it must have exactly three live neighbors.
Program features:
1. The number of grid levels and the number of free vertical settings (at least 2, which is 2* 2 grid)
2. Random initialization cells (the probability Platform: |
Size: 106496 |
Author:iDo4LT |
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Description: 这里面的代码是2DPCA的。这篇文章发表在PAMI上,Two-Dimensional PCA:
A New Approach to Appearance-Based
Face Representation and Recognition.google搜索已经引用681次。这里的代码是原作者的。这个分类不太好，语音识别是最近的，这是模式识别里的经典算法。-matlab code of 2DPCA Platform: |
Size: 2048 |
Author:Gu Zhenghong |
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Description: 自己编写的二维PCA代码。运行良好，欢迎下载者批评指正。-I have written code for a two-dimensional PCA. Running well, welcome to download criticized correction. Platform: |
Size: 1024 |
Author:binbin |
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Description: 线性判别分析(LDA)是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人脸识别
会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于I,DA的人脸识
别 文章对几种基于LDA的人脸识别方法做了理论上的比较和实验数据的支持，这些方法包括Eigenfaces、Fish—
erfaceS、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法。-Low—dimensional feature representation with enhanced discriminatory power is of paramount importance to face
recognition(FR)system．Linear Discriminant Analysis(LDA)is one of the most popular linear classification techniques of
feature extraction。but it will meet two problems as computational challenging and “small sample size’’when applying to
face recognition directly．After studying people solve the two problems through several ways and realize the face recogni—
tion based on LDA． The short paper here makes compare on theory and experimental data analysis on several Face
Recognition system using LDA—Based Algorithm，such as Eigenfaces(using PCA)，Fisherfaces，DLDA，VDLDA and VD—
FLDA．The experimental results show that the VDFLDA method is the best of al1． Platform: |
Size: 222208 |
Author:费富里 |
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Description: 二维pca算法 由南理工的杨健老师提出的-Two-dimensional pca algorithm of Yang Jian, a teacher from the proposed Huanan Platform: |
Size: 10240 |
Author:俞振洲 |
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Description: 二维主成分分析方法在人脸识别中的研究，MATLAB开发环境开发-Two-dimensional PCA methods for face recognition research, MATLAB development environment, development Platform: |
Size: 6144 |
Author:李豆豆 |
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Description: 此代码为matlab代码，分为两个部分。第一部分实现K均值聚类算法应用它来压缩图像。在第二部分中，你将使用主成份分析法pca来实现人脸图像的低维表示。
-This code for the matlab code, is divided into two parts. The first part of the implementation of the K means clustering algorithm to compress the image. In the second part, you will use the principal component analysis method PCA to realize the low dimensional representation of the face image. Platform: |
Size: 11510784 |
Author:pudnkobe |
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Description: Matlab 平台使用2DPCA对二维图像数据进行降低图片的维度。提高图像处理速度。(The Matlab platform uses 2DPCA to reduce the dimension of the image data for two-dimensional image data. Improve the speed of image processing.) Platform: |
Size: 4096 |
Author:S.Juy
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Search - matlab two dimensional pca