Description: 站长！这是使用LDA和PCA模式识别方法对人脸特征进行提取计算的Matlab程序框架，比较好！-head! This is the LDA and the use of pattern recognition methods on PCA Facial Feature Extraction for the Matlab program, good! Platform: |
Size: 3072 |
Author:Jesson |
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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: 从前用matlab编的人脸识别的特征提取以及识别程序，希望有用-The previous series of face recognition using matlab feature extraction and identification procedures, seek to help Platform: |
Size: 1024 |
Author:Bryan |
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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直接用于人耳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LDA的人耳识别。文章对几种基于LDA的人耳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Fisherears、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法
-Linear Discriminant Analysis (LDA) is a more common linear feature extraction for classification. LDA but used directly in the human ear to identify problems encountered dimension and the Platform: |
Size: 2129920 |
Author:冷福 |
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Description: 该程序包实现了模式识别中的两个特征提取算法，主成分分析PCA和线性判别分析LDA。采用C++语言编写，开发环境VS。 程序包还提供了两个测试样本文件。-The package to achieve the recognition of the two feature extraction algorithm, principal component analysis PCA and linear discriminant analysis LDA. Using C++ language, development environment VS. Package also provides two test samples. Platform: |
Size: 46080 |
Author:tangxiaojun |
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Description: 子空间学习的代码，主要包括人脸识别中常用的特征提取算法如pca lda 以及目前常见的流行学习的相关代码-Subspace learning the code, mainly including commonly used in face recognition feature extraction algorithms such as pca lda and the current prevalence of common learning-related code Platform: |
Size: 173056 |
Author:dd |
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Description: 编程是由pca和lda结合的脸部特征提取，用于三维图像。-multilinear principal component analysis combined with Linear discriminant analysis 3D face feature extraction. Platform: |
Size: 4096 |
Author:tai shu worn |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition Platform: |
Size: 2048 |
Author:sofi |
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Description: lda线性特征提取，用于人脸识别，首先进行小波特征提取后用lda提取特征。-lda linear feature extraction for face recognition, first of all, after feature extraction using wavelet feature extraction using lda. Platform: |
Size: 2048 |
Author:gu |
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Description: Kernel Discriminant Analysis a kernel extension of Linear Discriminant Analysis technique which is a well-known feature extraction technique.-Kernel Discriminant Analysis is a kernel extension of Linear Discriminant Analysis technique which is a well-known feature extraction technique. Platform: |
Size: 3072 |
Author:rem |
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Description: csp特征提取，lda进行分类，四分类脑电信号处理，亲测可用(CSP feature extraction, LDA classification, four classification of EEG signal processing, pro test available) Platform: |
Size: 23570432 |
Author:Lonathe |
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Search - feature extraction LDA