搜索资源列表
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Sparse Representation or Collaborative Representation: Which Helps Face Recognition? This code devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classificati
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ma yi sparse representation classification .EXTENDED YALE B database.recognition rate 95 。-ma yi sparse representation classification. recognition rate 95 .
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求解l1范式的值，用于压缩感知中的稀疏表示。进行分类-Solving the value of l1 paradigm for compressed sensing of sparse representation. Classification
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人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-Recently the sparse representation (or codin
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Sparse Representation for accurate classification of corrupted and occluded facial expressions使用稀疏表示方法对有遮挡和腐蚀的人脸表情图像进行分类-Sparse Representation for accurate classification of corrupted and occluded facial expressions
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运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。-Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training a
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based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the mini
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based classification (SRC) has been widely used for face
recognition (FR). SRC first codes a testing sample as a
sparse linear combination of all the training samples, and
then classifies the testing sample by evaluating which class
leads to
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本matlab源程序适用于论文 metasample-based sparse representation for tumor classification
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本程序适用于论文metasample-based sparse representation for tumor classification-this code suite for paper metasample-based sparse representation for tumor classification
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此程序为袁晓彤CVPR论文“基于多任务联合稀疏表示的的视觉分类”的主代码部分-The program for Yuan Xiaotong CVPR paper " visual classification with Muti-task joint sparse representation" main code of the parts
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matlab code of A hybrid approach combining extreme learning machine and sparse representation for image classification. Engineering Applications of Artificial Intelligence 27 (2014): 228-235.-Luo, Minxia, and Kai Zhang. A hybrid approach combining e
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极限学习机\极速学习机\ELM
稀疏表示人脸识别\稀疏表示\L0范数求解
基于ELM与稀疏表示的混合人脸识别算法
AR人脸识别准确率95 .
文章:Luo, Minxia, and Kai Zhang. A hybrid approach combining extreme learning machine and sparse representation for image classification. Engineering Applications of Artific
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Sparse Representation based Fisher Discrimination Dictionary
Learning for Image Classification
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这篇文章主要是讲高光谱分类的，使用基于词典的稀疏算法对高光谱进行分类。-Dictionary-Based, Clustered Sparse Representation for
Hyperspectral Image Classification
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matlab中的cvx工具包,适用于windows64位系统，在信号的稀疏表示分类领域有很大的用处，网络上有此工具包的安装说明，1要先解压2移动到matlab的一个空文件夹下3打开matlab，将当前路径设置为此文件夹下的cvx文件夹4在命令行窗口输入cvx_setup-matlab in cvx toolkit for windows64 bit systems, in a sparse representation of the signal areas of the classificat
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利用超限学习机（ELM）和稀疏表示(SRC)进行图像分类。Matlab完整源码。-Extreme learning machine and adaptive sparse representation for image classification
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K-SVD可以看做K-means的一种泛化形式，K-means算法总每个信号量只能用一个原子来近似表示，而K-SVD中每个信号是用多个原子的线性组合来表示的。
K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分类等应用。(K-SVD can be regarded as a generalized form of K-means. The total K-means algorithm can only approximate one signal for each sem
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稀疏表示分类器应用于高光谱图像分类的MATLAB代码实现(MATLAB Code Implementation of Sparse Representation Classifier for Hyperspectral Image Classification)
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