Description: 二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机
(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽
略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸
识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem of LDA algorithm can be overcome by two—dimensional LDA f 2DLDA)，and
Support Vector Machine(SVM)has the characteristic of structural risk minimization．In this paper，two methods were
combined and used for face recognition．Firstly，the original images were decomposed into high—frequency and low—frequency
components by Wavelet Transform(WT)．The high—frequency components were ignored，while the low—frequency components
can be obtained．Then．the liner discriminant features were extracted by 2DLDA，and”one VS rest”。strategy of SVMs for
muhiclass classification was chosen to perform face recognition． Experimental results based on ORL f Olivetti Research
Laboratory1 face database and Yale face database show the validity of 2DLDA+SVM algorithm for face recogn ition． Platform: |
Size: 236544 |
Author:费富里 |
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Description: This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It can well characterize the local scatter as well non-local scatter which simultaneously maximizes the non–local scatter and minimizes the local scatter. This makes UDP more powerful and more intuitive than LDA and PCA. This makes UDP a good choice for real-world biometrics application The proposed method is applied to palm biometrics and is examined by small set of samples per class.
-This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It can well characterize the local scatter as well non-local scatter which simultaneously maximizes the non–local scatter and minimizes the local scatter. This makes UDP more powerful and more intuitive than LDA and PCA. This makes UDP a good choice for real-world biometrics application The proposed method is applied to palm biometrics and is examined by small set of samples per class.
Platform: |
Size: 89088 |
Author:hello |
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Description: 没有使用matlab自带工具包，而是使用了自写的PCA，来用其对数据进行线性分类。-without using the toolbox given by matlab，i write pca myself and implement it into LDA classification. Platform: |
Size: 1024 |
Author:Olivia |
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Search - classification by LDA