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 understand the specific tenets who can
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.
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 Component Analysis based on the proce
小波降噪Matlab
- 设定信噪比和随机数种子,使用确定阈值、软阈值由一维小波函数对信号实施降噪。
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+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)
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 in the low-dimensional space, b
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)
tsne降维算法
- tsne降维算法,目前最好用的降维方法之一 可直接使用
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
核主元分析(Kernel principal component analysis ,KPCA)在降维、特征提取以及故障检测中的应用
- 主要功能有: (1)训练数据和测试数据的非线性主元提取(降维、特征提取) (2)SPE和T2统计量及其控制限的计算 (3)故障检测 KPCA的建模过程(故障检测): (1)获取训练数据(工业过程数据需要进行标准化处理) (2)计算核矩阵 (3)核矩阵中心化 (4)特征值分解 (5)特征向量的标准化处理 (6)主元个数的选取 (7)计算非线性主成分(即降维结果或者特征提取结果) (8)SPE和T2统计量的控制限计算