搜索资源列表
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
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
- 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
PCA
- 用于模式识别中的PCA降维输入数据data和option。data是一个矩阵，每一行代表一个样本。option是选择降维到多少维。-[eigvector, eigvalue] = PCA(data, options) [eigvector, eigvalue] = PCA(data)
PCA
- 主成分分析的代码，降维的工具，特征提取降维的工具-PCA code
pca
- PCA降维方法,这是一个针对图像处理的PCA降维处理方法-The method of PCA,whic is used in the image processing.
pca-deductional-vector
- pca降维 在pca提取vd后可以利用降维进行更加简便的操作-pca pca extraction vd dimension reduction in the dimensionality reduction can be used after a more simple operation
PCA
- 模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维，然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码，又有自己写的报告，还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释，很容易懂。最后结果输出到txt文件中。-Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduct
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,
11数据降维_配套代码
- 这是吴恩达在course公开课上讲的数据降维的作业的代码，主要是应用PCA对数据降维(This is Wu Enda in the course open class lectures on data dimension reduction operations code, mainly using PCA for data dimensionality reduction)
PCA
- 采用INP数据（145*145*200），该数据有16个类别， PCA进行数据降维，然后对降维数据采用kNN分类（k=1）。(Using INP data (145*145*200), the data has 16 categories, PCA carries out data reduction, and then uses kNN classification for dimensionality reduction data (k=1).)
PCA0118
- PCA降维，将特征以二维矩阵形式输入，对特征进行降维处理。(PCA dimension reduction, the characteristics of a two-dimensional matrix input, the feature dimensionality reduction.)
pca
- 大数据降维方法，具体的处理了图像等，包括数据的冗余部分，利用PCA技术快速降维。(Large data dimensionality reduction method.Specifically dealing with images, including redundant parts of data, and using PCA technology to reduce dimensionality rapidly.)
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.)
PCA+SVM
- 先用PCA降维，在利用支持向量机进行分类，这个分类是二分类，所以PCA的降维降到两维即可分类。(Firstly, PCA dimensionality reduction is used to conduct classification with support vector machine. This classification is binary classification, so the dimensionality reduction of PCA can be reduced t
PCA
- 实现图片处理的传统pca降维，需要自己改文件路径(To reduce the dimension of traditional PCA in image processing, we need to change the file path by ourselves)
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