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Dimension-reduction--toolbox
- 该工具箱中包含了多种降维算法。其中有传统的PCA和Local PCA算法，也有典型的流形学习算法，如Isomap、LLE、HLLE、Laplacian Eigenmaps 和 Local Tangent Space 。-The toolbox contains a variety of dimensionality reduction algorithms. In which the traditional PCA and Local PCA algorithms, there are the
empca2.tar
- 模式分类中应用到的PCA算法，包括其奇异值分解SVD算法。可用来降维提取主元素等。-pattern classification applied to the PCA algorithm, including its SVD singular value decomposition algorithm. Can be used to take down the main Viti Levu and other elements.
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
PCA
- 主成份分析，一个最经典的无监督学习算法，也是最常用的线性降维方法-PCA
pcasearch
- 基于焊接图片的pca降维，knn分类算法。-Pca-based solder image dimension reduction, knn classification algorithm.
PCA
- 自己写的PCA降维算法，还有模糊k均值大家可以参考一下-Write their own PCA dimensionality reduction algorithm, and fuzzy k means we can refer to
pcapro
- 基于主成分分析的PCA算法，利用进行各类图像降维，达到精简计算过程的目的。-PCA based on principal component analysis algorithm for various images using dimensionality reduction to achieve the purpose of streamlining the calculation.
pca
- 实现pca功能，进行数据降维，使算法简单化-Realize pca functions, to data dimension reduction, the method is simple
pca
- 可以很好的实现流形学习算法中的线性降维算法PCA数据降维。-Can well realize the manifold learning algorithm of linear dimension reduction algorithm of PCA data dimension reduction.
K-Means PCA降维
- K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the character
pca降维算法
- pca降维算法，试验已经成功，将39维数据降到12维(PCA dimensionality reduction algorithm, the test has been successful, the 39 dimensional data down to 12 dimensions)
pca降维
- pca数据降维算法，很好的解决数据灾难的问题。(PCA data dimensionality reduction algorithm, a good solution to the problem of data disaster.)
PCA
- 简单的数据降维算法（PCA）举例，构造随机的10维数据，降维成3维的。Sample可替换成用户数据(Examples of simple data reduction algorithms (PCA) are presented)
PCA实现特征降维
- pca和_fase_pca实现特征降维，两种算法都有所改进，特别是pca可以根据自己的需要进行相应的改进，代码清晰易懂，希望对你有帮助。(PCA and _fase_pca to achieve feature reduction, the two algorithms have improved, especially PCA can be improved according to their needs, the code is clear and easy to understand,
PCA
- PCA 算法演示 主要用于数据进行降维处理(PCA suanfa zhuyaoyongyushujujinxingjiangweichuli)
PCA
- 用matlab自带的PCA算法对图像进行降维(Dimensionality reduction for images)
pca_PCA降维
- 一款很好用的PCA降维算法，可以自己修改后随意使用。(A very good PCA dimensionality reduction algorithm.You can modify it yourself and use it at will.)
NM_PCA
- PCA降维算法，本程序已经调好，可以直接跑数据(PCA dimension reduction algorithm, this program has been adjusted, you can run data directly)
降维code
- 了解降维、特征筛选等基本原理 掌握PCA、SVD、LAD和NMF等算法实现及应用(Understand the basic principles of dimensionality reduction and feature selection Master the algorithm implementation and application of PCA, SVD, lad and NMF)
PCA+mnist
- 基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。 经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set. After PCA dime