Principal Components Analysis

A script containing the methods used to develop a principal components analysis in MATLAB is presented. The results are analyzed and plotted in different ways in order to interpret the information. The script is presented in Spanish and English. Some datasets are attached and ready to... Platforms: Matlab

Orthogonal Linear Regression in 3D-space by using Principal Components AnalysisThis is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components... Platforms: Matlab

Do principal components analysis (PCA) on real-valued data.Two methods are available: 'eig' and 'svd' which solve the problem by eigenvalue decomposition and singula value decomposition, respectively. Please note that 'svd' is running in 'economy' mode. Platforms: Matlab

PickStock (copyright 2003, Bell Software and Services, Inc.) uses principal components analysis to search out potentially underpriced stocks by analyzing a user-supplied database of historical stock prices. The program is believed to be an implementation of what is known in the financial market... Platforms: Windows

MPCA is a comprehensive suite of tools for doing discrete principal components analysis on data sets of size 100Mb or more. Scaling is done using sparse vectors, multi-threading, memory mapping, and other POSIX tricks. Reports, file dumping utilities and other utilities are included. The... Platforms: *nix

Made a PCA block (principal component analysis) within Simulink. Block calculates the principle components on any sized input matrix.Equivalent of the MATLAB PRINCOMP command (currently only outputs PC). Platforms: Matlab

Matlab source codes for Multilinear Principal Component Analysis (MPCA)%[Algorithms]%The matlab codes provided here implement two algorithms presented in the paper "MPCA_TNN08Jan.pdf" included in this package:Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal... Platforms: Matlab

This m-file give you the Nipals algorithm to realize a Principal Components Analysis for statistical study. I realize this program specifically with an aim of analysis spectroscopic data. Use 'help' for description Platforms: Matlab

Choice Components Analysis analyzes survey data on the primary determinants of option choice. These determinants are automatic and blocked selections, and, comparative evaluations between options. Habits and behavioural scripts are examples of automatic selections, while, blocked selections occur... Platforms: Windows

The Unscrambler® is the complete multivariate analysis and experimental design software, equipped with powerful methods including Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR), Partial Least Squares Regression (PLS-R), 3-Way PLS Regression, K-Means Clustering and SIMCA... Platforms: Windows

XLSTAT is a complete analysis and statistics add-in for Excel. It has been developed since 1993. XLSTAT includes more than 240 features in general or field-specific solutions. The use of Excel as an interface makes XLSTAT a user-friendly and highly efficient statistical and multivariate data... Platforms: Windows, Windows 8, Windows 7, Windows Server

This toolbox includes routines for using principal component analysis (PCA) and independent component analysis (ICA) to extract cellular signals from imaging data sets. A full description and validation of the method is provided in the paper, "Automated Analysis of Cellular Signals from... Platforms: Matlab

Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation called canonical discriminant analysis. It derives the canonical coefficients parallels that of one-way MANOVA and it finds linear combinations of the quantitative... Platforms: Matlab

CurlyWhirly is a very simple Java 3D graph viewer suited for viewing any kind of data in three dimensions. This includes, but is not restricted to, output from Principal Components Analysis or Principal Coordinate Analysis. CurlyWhirly is a Java Swing application and uses the Java 3D API to... Platforms: Mac

It involves a mathematical procedure that transforms a number correlated variables into a smaller number of uncorrelated variables called principal components.Databes included with code. Requirements: - MATLAB 7.0.1 or higher Platforms: Windows, Mac, *nix, Matlab, BSD Solaris

Modular toolkit for Data Processing (MDP) is a Python data processing framework. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA),... Platforms: Windows, Mac, *nix, Python, BSD Solaris

This code used to learn and explain the code of PCA to apply this code in many applications.For nay help or question send toengalaatharwat@hotmail.com Platforms: Matlab

This m-file deals with the principal component solution of the factor model thru the complete data matrix, the latent root criterion, and uses the varimax factor rotation. Optionally, it also gives the residual matrix, which results of the difference between the original correlation and the... Platforms: Matlab

The purpose of Discriminant Analysis Programme (DAP) is to facilitate discrimination and classification of (to be) grouped data with robust estimation- and modeled structures for the covariances in a one-go software. The robust estimation methods are the S-estimator and Donoho-Stahel estimator.... Platforms: Matlab

XLSTAT is a complete analysis and statistics add-in for MS Excel. It includes regression (linear, logistic, nonlinear), multivariate data analysis (PCA, DA, CA, MCA, MDS), correlation tests, parametric tests, non parametric tests, ANOVA, ANCOVA and much more. Complementary modules are available... Platforms: Windows