# Dimension reduction projects and source code

# Mrmr feature selection (using mutual information computation) in matlab

# Minimum redundancy maximum relevance feature selection in matlab

# Generalized principal component pursuit in matlab

# Improvd downward branch and bound algorithm for regression variable selection in matlab

# Robust multidimensional scaling (mds) using ecdfs in matlab

# Low rank multivariate autoregressive model for dimensionality reduction in matlab

# Recovery of low rank and sparse matrix in matlab

# Efficient multidimensional scaling (mds) in matlab

# Independent Component Analysis Matlab Code

Independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.

# Feature selection using matlab

# Principal Component Analysis Matlab Code

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.

# Multiple correspondence analysis based on the burt matrix. in matlab

# Multiple correspondence analysis based on the indicator matrix. in matlab

# Correspondence analysis. in matlab

# Rafisher2cda in matlab

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

# Expectation-maximization principal component analysis in matlab

# Factor analysis by the principal components method. in matlab

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

# Multilinear principal component analysis (mpca) in matlab

# Information theoretic feature selection in matlab

# Pca Matlab Code

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.