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Description: 这是一个简单明了的程序,供那些初级汇编程序员直接使用.-This is a simple and straightforward procedure for the initial compilation of programmers who directly use.
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Size: 6144 |
Author: 刘雷 |
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Description: PCA/LDA/LPP/TensorLPP/代码。
LPP是目前一种比较重要的子空间算法。基于Tensor的子空间算法，是传统PCA/LDA算法的进一步推广，具有重要意义。-PCA/LDA/LO/TensorLPP/code. Alignment is a more important subspace algorithm. Based on the Tensor subspace algorithm, is a traditional PCA/LDA further promote algorithm is of great significance.
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Size: 16384 |
Author: 李民 |
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Description: 2维LPP（2维局部映射保护）代码，可以对图像等二维数据进行降维。-two-dimensional LCDs (two-dimensional mapping of local protection) code, two-dimensional images of data dimensionality reduction.
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Size: 1024 |
Author: 李民 |
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Description: LPP for matlab, Good code for you!-LCDs for Matlab, Good code for you!
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Size: 2048 |
Author: stone |
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Description: 一个用来计算高维数据线性图形嵌入的matlab函数，是几乎所有线性降维算法（如LPP, NPE, IsoProjection, LSDA.）都要用的一个文件，在这里上传给大家，一定用得着！-A high dimensional data used to calculate the linear graphics embedded matlab function, is that almost all linear dimensionality reduction algorithms (such as LPP, NPE, IsoProjection, LSDA.) Should be used in a document From here to the U.S., we use gained!
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Size: 3072 |
Author: ttt |
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Description: 特征提取是人脸识别技术中一个基本而
又十分重要的环节，寻找有效的特征是解决识别问题的关键本文提出了一种新的人脸特征提取方法。该方法通过可调因子有效结合人脸局部流形结构信息和样本
的类别信息，充分提取样本的判别信息，将LPP 发展成为有监督方法。实验结果表明，该方法具有较好的
识别效果。-Feature Extraction in Face Recognition Technology is a fundamental and very important to find the characteristics of an effective solution to identify the crux of the problem is proposed in this paper a new facial feature extraction methods. This method is through the effective integration of tunable factor partial face manifold structure of information and samples of the types of information, the full extraction of the sample discriminant information, will be developed into a supervised LPP method. Experimental results show that the method has better recognition effect.
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Size: 173056 |
Author: 张波 |
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Description: PCA,LDA,LPP三种人脸识别的算法-PCA, LDA, LPP three face recognition algorithms
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Size: 16384 |
Author: wj |
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Description: 用于图像处理，人脸识别等-For image processing, face recognition, etc.
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Size: 2048 |
Author: wei |
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Description: 核lpp（局部保持映射）的降维方法。跟Xiaofei He的论文配套-Nuclear lpp (partial maintain mapping) methods of dimensionality reduction. Xiaofei He told the paper supporting
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Size: 2048 |
Author: 小哈 |
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Description:
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Size: 1024 |
Author: 王博 |
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Description: 这是一篇有关于LPP（局部保持投影）的英文资料-It is papar about LPP
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Size: 165888 |
Author: zang |
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Description: 一种很重要的非监督降维方法，是流形学习算法Laplacian Eigenmap 的线性化方法，在人脸识别中效果非常好。-A very important method of unsupervised dimensionality reduction, manifold learning algorithm is Laplacian Eigenmap linearization method is very effective in face recognition.
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Size: 1024 |
Author: 仲国强 |
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Description: 流形学习MATLAB代码，HE 的一个最经典的算法，是线性子空间学习的基本算法。-LPP
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Size: 2048 |
Author: lai_zhi_hui |
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Description: 基于LPP的人脸识别模块，运用matlab7.0编写，识别率达到70 以上，能够很好的识别不同姿势，光照，表情的变化-LPP-based face recognition module, using matlab7.0 writing, over 70 recognition rate can be a very good identification of the different positions, illumination, expression changes in
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Size: 5120 |
Author: 12 |
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Description: 本代码实现局部保持映射lpp算法。主要功能是保持高维空间的局部特性-This code implements partial mapping lpp algorithm to maintain. Main function is to maintain a high-dimensional space of the local characteristics of
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Size: 2048 |
Author: 刘国胜 |
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Description: Laurens van der Maaten 编写的LPP算法，与 Deng Cai版本不同，大家比较下-This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7b.
(C) Laurens van der Maaten
Tilburg University, 2008
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Size: 32768 |
Author: 王光 |
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Description: 经典算法LPP算法基于java语言的实现。-LPP algorithm is based on the classical algorithm implementation java language.
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Size: 31744 |
Author: 陈朴 |
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Description: This file implement lpp algorithm for dimension reduction and clustering techniques.
Dimension reduction, Clustering.
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Size: 1024 |
Author: matin |
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Description: LPP是局部保局投影方法，这里给出了matlab的源码-lpp code for face recognition
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Size: 2048 |
Author: 醉清风 |
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Description: lpp算法实现,Locality Preserving Projections 局部保持投影算法 hexiaofei的论文(lppLocality Preserving Projections (LPP) builds a graph incorporating neighborhood information of the data set. Using the notion of the Laplacian of the graph, we then compute a transformation matrix which maps the data points to a subspace. This linear transformation optimally preserves local neighborhood information in a certain sense. The representation map generated by the algorithm may be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold .The new algorithm is interesting from a number of perspectives)
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Size: 1024 |
Author: kazi
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