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NMFs-LDA
- 基于NMFs（非负矩阵稀疏分解）和LDA（线性辨别分析），是一种新的雷达目标一维距离像识别方法。
nmfpack
- NMFs算法（带稀疏度约束的非负稀疏矩阵分解）用于实现基于人脸局部特征的人脸识别，通过近似的矩阵分解进行空间降维。
LNMF
- 人工智能模式识别中基于非负矩阵分解生成特征空间的算法-artificial intelligence pattern recognition based on non-negative matrix factorization generation features of the algorithm space
ear5.rar
- IEEE上关于人耳图像识别的论文：使用改进的非负矩阵分解的人耳识别,Ear Recognition using Improved Non-Negative Matrix Factorization
NMF
- 非负矩阵分解的人脸识别NMF 可正常运行 算法源码-Non-negative matrix factorization NMF for face recognition algorithms can be the normal operation of source
PIE1
- 基于双权重非负矩阵分解的人脸识别Matlab code-double weight NMF for face recognition
DISCRIMINANTSPARSENONNEGATIVEMATRIXFACTORIZATION.r
- 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
Nonnegative_matrix_factorization.tar
- Nonnegative_matrix_factorization是实现非负矩阵分解的程序，该算法可以用来进行图像分解和模式识别 -Nonnegative_matrix_factorization is to achieve non-negative matrix factorization procedure, the algorithm can be used for pattern recognition and image decomposition
results
- 非负矩阵分解，分解,数据压缩，文件检索，图像识别-NMF，there is NMF，I am good
paper4
- 基于小波变换和二维非负矩阵分解的人脸识别算法.pdf-Based on wavelet transform and two-dimensional non-negative matrix factorization algorithm for face recognition. Pdf
NMF
- 非负矩阵Non-negative分解的人脸识别NMF 可正常运行.-Non-negative matrix factorization NMF for face recognition algorithms can be the normal
qqqqq
- 为了直接对内燃机振动谱图像进行诊断识别，提出一种基于改进变分模态分解（ＶＭＤ）、伪魏格纳时频分 析（ＰＷＶＤ）与局部非负矩阵分解（ＬＮＭＦ）的内燃机振动谱图像识别诊断方法-In order to direct the internal combustion engine vibration spectral image for diagnosis recognition is proposed based on the improved variational mode decomposit
NeNMF_code
- 非负矩阵分解的最新算法的matlab实现，以及多种算法之间的对比，和在图像识别中的应用,实测可用-The latest non-negative matrix factorization algorithm matlab implementation, as well as the contrast between a variety of algorithms and image recognition applications, the measured available
data_batch_2
- cifar-10数据集由10个类的60000个32x32彩色图像组成，每个类有6000个图像。有50000个训练图像和10000个测试图像。数据集分为五个训练批次和一个测试批次，每个批次有10000个图像。测试批次包含来自每个类别的恰好1000个随机选择的图像。训练批次以随机顺序包含剩余图像，但一些训练批次可能包含来自一个类别的图像比另一个更多。总体来说，五个训练集之和包含来自每个类的正好5000张图像。 具体：batch2.mat文件，该训练集可以用于图片识别，非负矩阵分解等。(The ci