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sparse representation algorithm
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Matching Pursuit方法，经典的稀疏表示方法，可以用人脸识别和图像分类，图像去噪，现在非常流行。-Matching Pursuit method, sparse representation of the classic, you can use face recognition and image classification, image denoising, now very popular.
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Abstract - A fl exible multiscale and directional representation for images is
proposed. The scheme combines directional fi lter banks with the
Laplacian pyramid to provides a sparse representation for two-
dimensional piecewise smoot
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介绍压缩传感理论中要用到的信号的稀疏表征原理-Introduced the theory of compressed sensing to use the sparse representation of signal theory
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Locally Adaptive Sparse Representation for Detection, Classification, and Recognition. Lectuures given by Prof Trac Tran from john Hopkins university
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稀疏表达：向量、矩阵与张量.doc，对于学习压缩感知、稀疏表示的人有帮助-Sparse expression: Vector, matrix and tensor. Doc, for learning compressed sensing, sparse representation of the people to help
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Sparse Representation and Its Applications
Yuanqing Li
Center for Brain Computer Interface &
Brain Information Processing
South China University of Technology,
Guangzhou, China
-Yuanqing Li
Center for Brain Computer Interface &
Bra
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主要用于解决模式识别中稀疏表示人脸识别核心问题L1范数源代码，程序采用同伦算法设计的，在目前稀疏表示多种算法中，同伦算法是性能公认最好的.-Mainly used to solve the sparse representation of face recognition pattern recognition in the core of L1 norm source code, the program designed using the homotopy algorithm, sparse
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稀疏表示应用研究 信号稀疏表示方法研究及国内外研究现状-the method of Sparse Representation
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Elad写的关于稀疏理论的书，内容丰富，适合初学稀疏理论的同学，不容错过额-a book about sparse representation of signal and its practice
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基于峰值变换的信号稀疏表示及重建，相关理论说明。-Sparse representation of signals based on peak transform and reconstruction, and related theoretical explanations.
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人脸稀疏表示的经典文献，做人脸稀疏表示必看-Face the classic literature on sparse representation, sparse representation of human face must-see
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含噪信号的稀疏表示研究，该方法的计算复杂度较低-Sparse representation of noisy signals, the low complexity of the method of calculation
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KSVD和MOD字典学习稀疏表示程序代码-KSVD, and MOD dictionary learning sparse representation of program code
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基于稀疏表示的逆合成孔径雷达自聚焦成像，讲述了自聚焦方法-Based on sparse representation inverse synthetic aperture radar imaging self-focusing
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稀疏表达及其应用的简单介绍，其中涵盖了稀疏表示、特征提取、压缩感知、图像增强、盲源分离、模式分类、目标跟踪和图像超分辨等。PPT和PDF是对应的，并添加了可视化的结果。-Sparse Representation and Its Application: Compressive Sensing, Visual Feature, Image Enhancement, Blind Source Separation, Pattern Classification, Object Tracking a
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this tutorial is about signal classification using sparse representation.
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完备图像稀疏表示是一种最新的图像表示模型，采用过完备字典中原子的线性组合形式实现图像的稀疏表示．传统
的过完备图像稀疏表示模型采用重建误差的平方和作为保真项．该保真项没有充分考虑到人眼对图像的感知特性，无法度量图
像中边缘、轮廓、纹理等局部几何结构的变化．本文基于过完备稀疏表示理论思想，建立了新的稀疏性正则化的图像稀疏表示模
型．模型中的正则项约束图像表示系数的稀疏性，保真项采用更符合视觉感知的结构相似性度量．基于正交匹配追踪算法，提出
了基于结构相似度的正交匹配追踪算法．实验结
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SPARSE REPRESENTATION CLASSIFIERS FOR SIGNAL REPRESENTATION
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3D数据稀疏表示算法及其软件流程的知识
好文章，推荐查阅学习
-good article about sparse representation
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