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判别稀疏非负矩阵分解，提出这个新算法，来进行人脸识别，比传统的NMF和一些其他的扩展算法效果好-Sparse non-negative matrix factorization judge proposed the new algorithm for face recognition, than the traditional extension of NMF algorithm and some other good results
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目前比较流行的稀疏分解重构程序，可以用在人脸识别、字典构造等方面。-Currently popular sparse decomposition and reconstruction process, can be used in face recognition, dictionary structure and so on.
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Sparse Representation or Collaborative Representation Which Helps Face Recognition
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illumination-robust face recognition via sparse representation
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文獻「Robust Sparse Coding for Face Recognition」
及matlab代碼-「Robust Sparse Coding for Face Recognition」 and matlab source code.
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參考文獻「Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary」and matlab code.-「Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary」and matlab code.
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稀疏表示人脸分类与识别，Mayi人脸分类识别框架，识别率非常高-sparse represention for face recognition
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本matlab程序适用于论文Robust Sparse Coding for Face Recognition
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Face recognition using L1 norm minimization 1.0 :Read the following paper for details of the algorithm - Robust Face Recognition via Sparse Representation by John Wright, Arvind Ganesh, and Yi Ma , Coordinated Science Laboratory, University of Illino
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Face recognition via Weighted Sparse Representation Face recognition via Weighted Sparse Representation
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源码实现了使用基于稀疏表示的人脸识别算法。使用GPSR作为l1模最小化方法。-Source code to achieve the use of sparse representation based on the face recognition algorithm. Using GPSR as a method for minimizing the L1 norm.
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