搜索资源列表
1kernel-ica1_1
- 核主元分析程序，利用核函数将非线性数据映射到线性空间，再利用主元分析提取特征数据。-KPCA procedure uses the kernel nonlinear data mapped into linear space, and then using principal component analysis to extract feature data.
kpca
- 这是一段核主元分析算法的源程序，仅供参考-This is a kernel principal component analysis algorithm source code, for reference only
backfitting
- 核主成分分析方法 可将低维空间数据转至高位空间的内积，再转至源空间-Kpca can be low to high dimension space data within the space of deposition, turn again to the source of space
cwstd
- 是基于主成成分和核主成成分的实例，有详细的注解，条理清晰易懂，适合初学者对pca与kpca的学习。-Is based on the principal as the main components and nuclear components as examples of the comments in detail, the clarity of easy-to-understand for beginners and pca learning kpca.
PCA-and-LDA
- 人脸识别经典资料，主要包含PCA，LDA,KPCA等方法-Face classic data consists mainly of PCA, LDA, KPCA methods
simfeat
- 一个包含常用特征提取方法的matlab工具包。内含 PCA, CCA, mnf, pls 、 KPCA, KCCA, kmnf, kpls 等算法的实现源码-a matlab toolkit Containing some common feature extraction methods . Containing PCA, CCA, mnf, pls, KPCA, KCCA, kmnf, kpls algorithm implementation source
pca_KPCA
- 一个pca和KPCA的基本介绍，详细介绍了其原理、区别、优缺点、应用范围等-Pca and KPCA a basic introduction, detailing its principle, distinction, advantages and disadvantages, application, etc.
SAR_KPCA
- 基于核函数的SAR图像目标识别,利用KPCA对SAR图像进行特征提取-SAR image target recognition based on kernel function，Use KPCA SAR image feature extraction
KPCA
- 核主成分分析是一种流行的非线性特征提取方法- Kernel principal component analysis is a popular nonlinear feature extraction method
kpca
- 核主成分分析方法是主成分分析的改进算法，其采用非线性方法提取主成分-Kernel principal component analysis method is an improved algorithm of principal component analysis, which uses a nonlinear principal components extracted
Kernel-PCA
- 一个老外写的文章，很不错，对以后学习核主元分析（KPCA）有帮助！-the paper was written by the foreigner，which is very useful and meaningful.
multiscaleKPCA
- 有关MKPCA进行故障检测的文章，在KPCA的基础上，进行分块处理。希望有用-About MKPCA fault detection article, based on KPCA, into blocks. I hope useful
dimen_toolbox
- 最新最强MATLAB降维工具箱，可用于人脸识别，模式识别，机器学习，数据挖掘，图像处理等领域，里面包含的算法有PCA,LDA,KPCA,KLDA,Laplacian，LPP,MDS,NPE,SPE,LLC,CFA,MCML,LM-The latest and greatest dimension reduction MATLAB toolbox can be used for face recognition, pattern recognition, machine learning, dat
hezhuchengfenxi-gist-kpca
- 它通过对原始数据的加工处理，简化问题处理的难度并提高数据信息的信噪比，以改善抗干扰能力。-It does this by processing the raw data, simplifies the problem and improve the signal to noise ratio of the data processing of information in order to improve anti-jamming capability.
kpca
- 主元分析法(PCA)是目前基于多元统计过程控制的故障诊断技术的核心，是基于原始数据空间，通过构造一组新的潜隐变量来降低原始数据空间的维数，再从新的映射空间抽取主要变化信息，提取统计特征，从而构成对原始数据空间特性的理解。-Principal Component Analysis
kpca
- 核主成分分析，用于轴承故障，人面识别，水位分布等的数据非线性提取。-Kernel principal component analysis for data bearing failure, human face recognition, water distribution and other non-linear extraction.
PatternRecognition
- (1)Bayes分类 已知N=9， =3，n=2，C=3,问x= 应属于哪一类？ (2)聚类 使用c－均值聚类算法在IRIS数据上进行聚类分析 (3)鉴别分析 在ORL或Yale标准人脸数据库上完成模式识别任务。 用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验。-(1) Bayes classification Known N = 9, = 3, n = 2, C = 3, x = should ask which cat
SoftCam
- pdf kpca component analyse principal kernel
exempleskpca
- many examples of kpca programs for nonlinear system
PCA-KPCA
- 主成分分析(Principle Component Analysis, PCA)是最为常用的特征提取方法，被广泛应用到各领域，如图像处理、综合评价、语音识别、故障诊断等。-Principal component analysis (Principle Component Analysis, PCA) is the most commonly used feature extraction methods are widely applied to various fields, such as