搜索资源列表
Matrix.rar
- 稀疏矩阵，非线性数据动态分类算法例子
MCALabWithUtilities
- MCA算法源程序，可用于稀疏分解，信号分类，图像修复，信号还原-MCA algorithm source code can be used for sparse decomposition, signal classification, image restoration, signal reduction
RVM_matlabToolBox
- 相关向量机(RVM)的matlab源程序，包含快速算法，内含代码使用说明。 RVM采取是与支持向量机相同的函数形式稀疏概率模型，对未知函数进行预测或分类。 优点： (1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布. (2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间. (3) RVM不需要估计过多的参数. (4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好. -Relevance Vector Ma
MatchingPursuits
- 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.
CVPR09-ScSPM
- 基于稀疏编码和线性塔式匹配的图像分类算法。-This package contains the Matlab codes implementing the ScSPM algorithm described in CVPR 09 paper "Linear Spatial Pyramid Matching using Sparse Coding for Image Classification".
loosematrix
- 稀疏矩阵，非线性数据动态分类算法例子-Sparse Matrix, nonlinear dynamic data classification algorithm example
l1_ls
- 稀疏表示分类算法，用于样本分类的数学算法-Sparse that classification algorithms, mathematical algorithms for sample classification
l1_ls
- 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
Face-image-classification-method
- 人脸稀疏分类研究，基于DD-DT CWT多字典的人脸特征稀疏分类方法-Face thinning classification, based on DD-DT CWT dictionary feature sparse classification method
Gabor
- 用Gabor滤波器和稀疏分类对表情进行识别-By Gabor filters and sparse facial expression classification identify
OMP0806
- 实现正交匹配追踪算法,能很好地实现稀疏分类结果，希望有帮助。-for doing orthogonal matching pursuit
eccv10_tutorial_part2
- 稀疏编码图像分类, 稀疏表示创始人写的PPT,内容精彩,分析清晰,易于理解(sparse coding image classification, PPT written by the original author of sparse representation, the PPT content is easy to realize with clear illustration and analysis.)
SRC_code
- 经过CSP 算法之后用于稀疏分类 此分类方法能达到较高的分类正确率(After the CSP algorithm is used for sparse classification, this classification method can achieve higher classification accuracy)
RSC
- 只要是使用稀疏表示方法进行的图像分类，非常有用！(It is useful as long as it is an image classification using the sparse representation method!)
AnalysisKSVDbox
- 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
SSC_1.0
- 稀疏子空间编码，实现高维数据的降维分类。(SUBSPACE SPARSE/SparseCoefRecovery)
Indian
- 使用基于词典的稀疏表示高光谱图像分类,多任务联合稀疏表示和逐步MRF优化的高光谱图像分类(Dictionary-based sparse representation hyperspectral image classification, multi-task joint sparse representation and stepwise MRF optimized hyperspectral image classification)
纹理特征
- 该算法实现了基于纹理特征的稀疏表示分类，代码注释清晰，容易理解(The algorithm implements the sparse representation based on texture features, and the code is clear and easy to understand.)
FDDL
- 基于Fisher字典学习的稀疏表示分类算法。(Sparse representation classification algorithm based on Fisher dictionary learning.)
SRC_SOMP_matlab-master
- 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)