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最热门的稀疏表示的算法，马毅等人在PAMI上发表的文章代码-Sparse representation of the most popular algorithms, Yi Ma, and others published an article in the PAMI code
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该文对压缩感知理论进行了综述，对压缩感知的稀疏表示、观测矩阵、编码、解码和有待研究的关键问题进行了综述-This paper summarizes the theory of compressed sensing, sparse representation of compressed sensing, observation matrix, encoding, decoding and the key issues to be examined were reviewed
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基于稀疏表示的人脸识别算法，是核心算法的演示版本-Face recognition algorithm based on sparse representation is the demo version of the core algorithm
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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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稀疏表示doa估计，l1范数凸优化的算法，性能很好-Low complexity method for DOA estimation
using array covariance matrix sparse
representation
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压缩感知的主要研究内容有信号的稀疏表达、观测矩阵的设计和信号恢复。精确的信号恢复算法是压缩感知中的关键。因此本文在压缩感知理论框架下研究恢复算法中的凸优化算法- Compressive sensing offers a variety of research fields, including signal sparse representation, sensing matrix design, signal reconstruction. Accurately signal reconstr
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各种稀疏表达，求优化问题，代入数据后可直接运行。-sparse representation
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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 classification (SRC John Wright CVPR2009) 实现-Sparse representation based classification (SRC John Wright CVPR2009)‘s codes
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稀疏人脸识别,来至论文Robust Face Recognition via Sparse Representation-Robust Face Recognition via Sparse Representation
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ICCV paper 《Fisher Discrimination Dictionary Learning for Sparse Representation 》 源码-ICCV paper 《Fisher Discrimination Dictionary Learning for Sparse Representation 》 CODE and pdf
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K-SVD算法用于稀疏表达,基于OMP算法。(The algorithm is apply for sparse representation)
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K-SVD算法用于稀疏表达，基于OMP算法。(The algorithm is apply for the sparse representation.)
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