搜索资源列表
IsomapR1
- 基于测地距离不变性的非线性降维算法源码，原文发表在2000年的Science杂志上，需要了解具体原理者请先看其论文。-based on geodesic distance invariance of nonlinear reduced dimension algorithm source code, the text published in 2000 in Science magazine, the need to understan
kpca_toy
- 基于kernel pca的非线性降维算法，原文发表于神经计算杂志上，有兴趣者可以先看论文。-PCA-based kernel of nonlinear reduced dimension algorithm, the original published in the Journal of neural computation, those interested can read papers.
ssvd-0.2.5.tar
- 是一个矩阵的奇异值分解算法的源码包，是在SVDPACKC的基础上进行的封装，典型应用是在LSI算法中用来降维生成一个小的语义空间。-is a matrix of the singular value decomposition algorithm source package is SVDPACKC conducted on the basis of the package, the typical application is in
somnet
- 一种通过自组织竞争学习网络实现数据降维和可视化的单层神经网络模型。用此算法可以把输入空间的多维映射到低维的（一维或者二维）的离散网络上，并将保持相同性质的输入数据在映射到低维空间时的拓扑一致性。iris以及letter两个数据集进行分类-A competitive learning through self-organizing network for data dimensionality reduction and visualiz
KPCAEXAMPLE
- 一个很好的核主成分分析matlab程序应用举例。该程序是在前人的核主成分分析程序基础上做了适当的修改产生的，可用于多维数据的降维和压缩处理。-A good kernel principal component analysis matlab application procedures, for example. The program is in the predecessors of Kernel Principal Compone
一维径向流程序
- 计算一维径向流的压力分布，产能计算，并形成最终可以直观观察的压降漏斗(Calculate the pressure distribution of one-dimensional radial flow, calculate the deliverability, and form the pressure drop funnel which can be observed visually)
code
- ssmfa将高光谱数据从高维观测空间投影到低维流形空间，达到约减数据维数的目的(ssmfa hyperspectral data is projected from the high dimensional observation space into the low dimensional manifold space, so as to reduce the dimensionality of data)
PCAjiangwei
- Gabor提取人脸图像特征后，PCA进行降低维数，(After Gabor extracts image features, PCA reduces dimensionality)
5.3 维纳滤波法
- 利用维纳滤波的方法进行语音信号的降噪处理，适用于语音增强领域(The denoising processing of speech signal by Wiener filtering is suitable for the domain of speech enhancement)
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)
空时自适应处理
- 仿真空时自适应处理STAP里的算法合集程序：Capon谱、降维算法3dt、JDL等(Algorithms aggregator for simulation space-time adaptive processing in STAP: Capon spectrum, dimension reduction algorithm 3dt, JDL, etc.)
KPCA
- KPCA算法属于非线性高维数据集降维，算法其实很简单，数据在低维度空间不是线性可分的，但是在高维度空间就可以变成线性可分的了(The KPCA algorithm belongs to the nonlinear high-dimensional data set dimension reduction. The algorithm is very simple. The data is not linearly separable i
SPA降维算法
- 连续投影算法，选取特征波长，可以实现多维数据的降维，提取特征波段
cars降维
- cars降维 matlab语言 有利于特征波长的提取
LLTSA降维
- 这个是KPCA核主成分分析的代码，好用，里面也带有范例(This is the KPCA kernel principal component analysis code, which is easy to use and also contains examples.)
tsneMATLAB论文仿真代码
- 实现tSNE降维，已经封装完成，可以直接在matlab使用(Reducing dimension of tsne)
1.pdf
- 特征降维的一种方法, 文章是英文文献，文章最后附有代码(a kind of method of feature dimensionality reduction)
3DT算法
- 该程序仿真了空时自适应处理STAP里的降维算法3dt，并与最优空时处理的结果进行了比较(This program simulates the space-time adaptive processing of the reduced-dimensional algorithm 3dt in STAP and compares the results with the optimal space-time processing.)
pca
- 应用于数据降维的一种MATLAB程序，可以实现从高维到低维的降解(A matlab program applied to data dimensionality reduction can realize the degradation from high dimension to low dimension)
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 handwr