Independent Component Analysis (ICA) in Matlab

Independent Component Analysis (ICA) in Matlab:

While performing data analysis tasks, it is generally assumed that the feature attributes are independent of each other. But data may not be available in such format. So it is very important to convert the feature attributes independent of each other. This can be done with a simple function for ICA in Matlab. This function will reduce the data into required number of feature attributes. 

ICA technique basically removes the linearity factor among the feature attributes. Matlab does not provide in built function for ICA. Various packages [1][2][3] of ICA in Matlab are available. It can also be termed as a method for dimensionality reduction. ICA basically projects the data into N number of dimensions. Note that ICA can only separate linearly mixed vectors. ICA assumes that the vectors are non-Gaussian in nature. 

[1] http://www.mathworks.com/matlabcentral/fileexchange/38300-pca-and-ica-package

[2] http://research.ics.aalto.fi/ica/fastica/

[3] http://jim-stone.staff.shef.ac.uk/bookmatlabcode.html