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/* Principal Components Analysis or the Karhunen-Loeve expansion is a classical method for dimensionality reduction or exploratory data analysis. One reference among many is: F. Murtagh and A. Heck, Multivariate Data Analysis, Kluwer Academic, Dordrecht, 1987 (hardbound, paperback and accompanying diskette). This program is public-domain. If of importance, please reference the author. Please also send comments of any kind to the author:
Update : 2008-10-13 Size : 5347 Publisher : lrx

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PyChem是用Python语言编写的多元变量分析软件。它包括一个前端图形界面用于管理和保存试验数据,并用多元分析的算法分析试验数据。主要的目录结构如下: PyChem\PURELIB\pychem 包括前端界面的管理和数据保存,以及核心的分析算法 PyChem\PURELIB\pychemlib 包括一些必要的支持库-PyChem Python language is used to prepare the multivariate analysis software. It includes a front-end GUI for the management and preservation of test data, and using multivariate analysis algorithm analysis of the test data. The main directory structure is as follows : PyChem \ PURELIB \ pychem including front-end interface management and data retention, and the core analysis algorithm PyChem \ PURELIB \ pychemlib including some necessary support libraries
Update : 2024-04-26 Size : 97280 Publisher : star

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GUI for Multivariate Image Analysis of 4-dimensional data (Matlab Code)-GUI for Multivariate Image Analysis of 4-d imensional data (Matlab Code)
Update : 2024-04-26 Size : 592896 Publisher : 廖智茗

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/* Principal Components Analysis or the Karhunen-Loeve expansion is a classical method for dimensionality reduction or exploratory data analysis. One reference among many is: F. Murtagh and A. Heck, Multivariate Data Analysis, Kluwer Academic, Dordrecht, 1987 (hardbound, paperback and accompanying diskette). This program is public-domain. If of importance, please reference the author. Please also send comments of any kind to the author:
Update : 2024-04-26 Size : 5120 Publisher : lrx

实用多元统计分析,在数据处理上很有用处。是超星的电子书-Practical multivariate statistical analysis, useful for data processing。It is an E-book of Superstar Browser。
Update : 2024-04-26 Size : 20663296 Publisher : 李洪涛

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用于特征降维,特征融合,相关分析等多元数据分析的典型相关分析Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis, canonical correlation analysis of Matlab code implementation.
Update : 2024-04-26 Size : 1024 Publisher : moxibingdao

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用于特征降维,特征融合,相关分析等多元数据分析的鉴别型典型相关分析(DCCA)Matlab代码实现。-For feature reduction, feature fusion, multivariate data analysis and correlation analysis based identification of canonical correlation analysis (DCCA) Matlab code implementation.
Update : 2024-04-26 Size : 1024 Publisher : moxibingdao

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用于特征降维,特征融合,相关分析等多元数据分析的广义典型相关分析(GCCA)Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis using generalized canonical correlation analysis (GCCA) Matlab code implementation.
Update : 2024-04-26 Size : 1024 Publisher : moxibingdao

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用于特征降维,特征融合,相关分析等多元数据分析的fisher鉴别分析(FLDA)Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis of the fisher discriminant analysis (FLDA) Matlab code implementation.
Update : 2024-04-26 Size : 1024 Publisher : moxibingdao

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用于特征降维人脸识别等多元数据分析的主分量分析投影的Matlab代码实现。-For feature reduction and other multivariate data analysis, face recognition principal component analysis projection of the Matlab code implementation.
Update : 2024-04-26 Size : 1024 Publisher : moxibingdao

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主元分析方法(PCA)是一种基于多元统计分析的数据降维方法, 它利用过程变量间的相关关系, 建立正常工况下的主元模型, 通过检验新的数据样本相对于主元模型的背离程度, 从而发现异常和故障。 -Principal Component Analysis (PCA) is based on multivariate statistical analysis of the data reduction method, which uses the correlation between process variables and establish the normal condition of the main element model, by testing samples of the new data relative to the principal component model departure from, and thus abnormal and failure.
Update : 2024-04-26 Size : 1024 Publisher : bonnie

PCA 应用于数据矩阵降维,压缩数据!广泛应用于各种行业。 Author: F. Murtagh- Principal Components Analysis or the Karhunen-Loeve expansion is a classical method for dimensionality reduction or exploratory data analysis. One reference among many is: F. Murtagh and A. Heck, Multivariate Data Analysis, Kluwer Academic, Dordrecht, 1987. Author: F. Murtagh
Update : 2024-04-26 Size : 5120 Publisher : zhengshu

A Guide to Multivariate Techniques Preparation for Statistical Analysis Review: ANOVA Review: ANCOVA MANOVA MANCOVA Repeated Measure Analysis Factor Analysis Discriminant Analysis Cluster Analysis
Update : 2024-04-26 Size : 202752 Publisher : Mike

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本书系统地介绍了生物医学数据分析的相关基础知识和主要的分析方法,并通过 MATLAB 对大量的 生物医学数据分析的实例进行实现。全书共分为 7 章:第 1 章绪论;第 2 章 MATLAB 应用基础;第 3 章 医用数学基础及其 MATLAB 实现;第 4 章多变量分析;第 5 章时间序列分析;第 6 章生物信息学基础; 第 7 章生物医学数据的检索方法。 -This book introduces the relevant basic knowledge and analysis of biomedical data analysis, and analysis of biomedical data through MATLAB instance implemented. The book is divided into seven chapters: Chapter 1 Introduction Chapter 2 based on MATLAB applications Chapter 3 Medical mathematical foundation and its MATLAB implementation Chapter 4 multivariate analysis Chapter 5, time series analysis Chapter 6 bioinformatics Chapter 7 of the foundation biomedical data retrieval methods.
Update : 2024-04-26 Size : 9374720 Publisher : double

MATLAB是一款优秀的数学计算软件,在各工程领域中应用都十分广泛。本书以MATLAB R2011a为基础,在第1版的基础上进一步介绍了MATLAB的概率与数理统计分析。本书以概率与数理统计的基本原理为线索,以MATLAB为辅助条件,分别介绍了概率与数理统计概述、统计估计、假设检验、方差分析、回归分析、正交试验分析、聚类分析、判别分析和多元数据相关分析等内容,理论与实践相结合,向读者演示了MATLAB在数理统计中的应用。-MATLAB is a mathematical calculation software, a wide range of applications in various engineering fields. Book based on MATLAB R2011a, the MATLAB version 1 on the basis of probability and mathematical statistics. Book as a clue to the basic principles of probability and mathematical statistics, MATLAB as auxiliary conditions were introduced Overview of probability and mathematical statistics, statistical estimation, hypothesis testing, analysis of variance, regression analysis, analysis of orthogonal test, cluster analysis, discriminant analysis and multivariate data analysis, theory and practice, and demonstrate to the reader of MATLAB in mathematical statistics.
Update : 2024-04-26 Size : 59392 Publisher : sl ss

Principal Component Analysis (PCA) is the general name for a technique which uses sophisticated underlying mathematical principles to transforms a number of possibly correlated variables into a smaller number of variables called principal components. The origins of PCA lie in multivariate data analysis, however, it has a wide range of other applications, as we will show in due course. PCA has been called, ’one of the most important results from applied linear algebra’[2] and perhaps its most common use is as the fi rst step in trying to analyse large data sets. Some of the other common applications include de-noising signals, blind source separation, and data compression.-Principal Component Analysis (PCA) is the general name for a technique which uses sophisticated underlying mathematical principles to transforms a number of possibly correlated variables into a smaller number of variables called principal components. The origins of PCA lie in multivariate data analysis, however, it has a wide range of other applications, as we will show in due course. PCA has been called, ’one of the most important results from applied linear algebra’[2] and perhaps its most common use is as the fi rst step in trying to analyse large data sets. Some of the other common applications include de-noising signals, blind source separation, and data compression.
Update : 2024-04-26 Size : 3080192 Publisher : Nurita

PCA应用于数据矩阵降维,压缩数据!广泛应用于各种行业。Author: F. Murtagh- Principal Components Analysis or the Karhunen-Loeve expansion is a classical method for dimensionality reduction or exploratory data analysis. One reference among many is: F. Murtagh and A. Heck, Multivariate Data Analysis, Kluwer Academic, Dordrecht, 1987.Author: F. Murtagh
Update : 2024-04-26 Size : 5120 Publisher : swhole

非常实用的数据分析参考,经典的多元数据分析电子书,第六版。-Very practical data analysis reference, the classic multivariate data analysis books, Sixth Edition.
Update : 2024-04-26 Size : 22932480 Publisher : liuwq

ata rich but information poor is an excellent way to characterize most chemical processes today. The lack of data analysis tools and adequate fundamental and experimental models makes it difficult to pursure product quality and im- proved understanding of a process. One data analysis tech- nique successfully applied in spectroscopy to reduce a large quantity of data into meaningful information is Chemometrics. Data when properly interpreted by statistical data analysis tools and fundamental and heuristic models yield meaningful infor- mation. In this paper we discuss the use of Chemometrics as a multivariate analyzer to provide a composite measurement of the state of a chemical process operation. An application of this analyzer on a Du Pont Plant is presented, and we introduce two measures to detect and identify important process shifts.-ata rich but information poor is an excellent way to characterize most chemical processes today. The lack of data analysis tools and adequate fundamental and experimental models makes it difficult to pursure product quality and im- proved understanding of a process. One data analysis tech- nique successfully applied in spectroscopy to reduce a large quantity of data into meaningful information is Chemometrics. Data when properly interpreted by statistical data analysis tools and fundamental and heuristic models yield meaningful infor- mation. In this paper we discuss the use of Chemometrics as a multivariate analyzer to provide a composite measurement of the state of a chemical process operation. An application of this analyzer on a Du Pont Plant is presented, and we introduce two measures to detect and identify important process shifts.
Update : 2024-04-26 Size : 677888 Publisher : JTNT

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模拟数据分析处理的过程,BP神经网络用于函数拟合与模式识别,现代信号处理中谱估计在matlab中的使用,可以动态调节运行环境的参数,线性调频脉冲压缩的Matlab程序,多元数据分析的主分量分析投影。- Analog data analysis processing, BP neural network function fitting and pattern recognition, Modern signal processing used in the spectral estimation in matlab, Can dynamically adjust the parameters of the operating environment, LFM pulse compression of the Matlab program, Principal component analysis of multivariate data analysis projection.
Update : 2024-04-26 Size : 12288 Publisher : mngfpn
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