搜索资源列表
PCA1
- pca算法，用于数据降维，注释非常详细清晰，-PCA algorithm, for data dimensionality reduction, clear and very detailed notes,
NeighborPreservEmbedd
- 一种邻域保护嵌入算法，用于高维数据的降维处理-The protection of a neighborhood algorithm for high dimensional data, dimensionality reduction to deal with
sne
- 一种基于概率的数据降维处理方法：Stochastic Neighbor Embedding-Based on the probability of data dimensionality reduction approach: Stochastic Neighbor Embedding
KLPP
- 核lpp（局部保持映射）的降维方法。跟Xiaofei He的论文配套-Nuclear lpp (partial maintain mapping) methods of dimensionality reduction. Xiaofei He told the paper supporting
LGE
- LGE算法（Linear Graph Embedding）用于降维，代码比较长，比较复杂。供大家研究！-LGE algorithm (Linear Graph Embedding) for dimensionality reduction, code longer, more complicated. For everyone!
OLPP
- 正交的Linear Graph Embedding算法！用于降维，供大家学习交流。-Orthogonal Linear Graph Embedding Algorithm! For dimensionality reduction for them to learn from the exchange.
MFA
- Marginal Fisher Analysis算法，可用于降维，注释有使用说明！供大家学习交流！-Marginal Fisher Analysis algorithm, can be used for dimensionality reduction, the Notes are used to explain! For all to learn!
pca
- 主成分分析程序，可用于数据降维及特征提取。-Principal component analysis procedures, can be used for data dimensionality reduction and feature extraction.
KPCA
- 一个很好的PCA程序。它可用于数据的降维，消噪及特征提取。-A good PCA procedures. It can be used for data dimensionality reduction, de-noising and feature extraction.
lda
- 非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
pca
- 非线性降维方法 可以应用于高维数据的机器学习-Nonlinear dimensionality reduction methods can be applied to high-dimensional data, machine learning
PCA
- 对输入的高维特征向量进行pca降维后输出低维的特征向量-PCA dimensionality reduction
empca
- EMPCA算法的函数代码，附带有训练测试数据集，用于特征降维等方面。-Algorithm EMPCA function code, attached to the test data set there is training for the characteristics of dimensionality reduction and so on.
drtoolbox.tar
- 这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值- ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhoug
mddm
- MDDM是一种多类标分类降维算法，使用成对约束对正类和负类进行迭代- is a novel algorithm to deal with the multilabel dimensionality reduction.
PCA_LDA
- 《机器学习》课上的作业，PCA和LDA降维，尽管网上很多，但很少注释，另外细节上也没注意。这里有很详细的注释。另外还附上一个Naive贝叶斯分类器，大家可以作比较。附带的图像包是OLR人脸。ReducedDim为想要提取的特征数，不是百分比！-" Machine learning" classes on the homework, PCA and LDA dimensionality reduction, even t
Mani
- 流形学习程序，Isomap，LLE，LTSA，etc，非线性数据降维-manifold learning, Isomap, LLE, LTSA, etc, nonlinear data dimensionality reduction
pca
- 运用pca算法降维，提取主要特征值，从而达到降维目的-Dimensionality reduction using pca algorithm, extract the main features of the value of
FunFDA
- FunFDA模式识别中特征提取中的数据降维的一种算法-fisher discriminant analysis
PCA
- 优化后的PCA 能对数据进行降维 很实用-PCA can be optimized for data dimensionality reduction is very useful