Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

The following matlab project contains the source code and matlab examples used for principal component analysis.

The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.

## Project Files:

Principal component spectral analysis in matlab

Multilinear principal component analysis (mpca) in matlab

Principal component analysis a simulink block

Uncorrelated multilinear principal component analysis (umpca) in matlab

Directional discrete cosine transform and principal component analysis based image fusion in matlab

Nipals algorithm for principle component analysis in matlab

Factor analysis by the principal components method. in matlab

Expectation-maximization principal component analysis in matlab

Dynamic pooled forecasting in matlab

Factor analysis by the principal components method without data. in matlab

Discriminant analysis programme in matlab

Fast adaptive coordinate descent for non linear optimization in matlab

Iterated principal factor method (principal axis factoring). in matlab

Noise level estimation in matlab

Iterated principal factor method (principal axis factoring) without data. in matlab

A benchmark software for multivariate statistical process control in matlab

Simulation of forward curve using pca (principle component analysis) in matlab

Toolbox for automated sorting of cellular calcium signals from optical imaging data. in matlab

Corresponding points through mdl in matlab

Rafisher2cda in matlab

World currents in matlab

Correspondence analysis. in matlab

Multiple correspondence analysis based on the indicator matrix. in matlab

Multiple correspondence analysis based on the burt matrix. in matlab