Independent Component Analysis (ICA) and Principal Component Analysis (PCA) are two popular signal processing algorithm. Can you please explain when we should apply these two algorithm and why ?
Thanks in advance for your valuable comments.
Hi,
These two methods are used in order to make a projection of the input signal and generate another signal in anthoer dimension, there are different applications but what I have seen like applications are :
Signal representation: When the both of PCA or ICA are applied, they represent the sgnal with coefficient that can be used to well represent the signal like a preprocessing step of pattern recognition system.
Feature reduction: When you have a signal which have too much data or a huge size, you want to reduce it in order to reduce the computig time and the rundandant data, one of the existing methods is using a projection metho like ICA and PCA which project the signal in another dimension which well separate the data in input and you can choose the amount of data you need.
There are other methods but I don't know them.
Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction. Given a set of multivariate measurements, the purpose is to find a smaller set of variables with less redundancy that would give as good representation as possible. The redundancy is measured by correlations between data elements. Therefore, the analysis can be based on second order statistics only. As PCA considers second order moments only it lacks information on higher order statistics.
Independent Component Analysis (ICA) is a technique data analysis accounting for higher order statistics. ICA is a generalisation of PCA. Moreover, PCA can be used as preprocessing step in some ICA algorithm. ICA has great potential in applications such as the separation of sound signals, in telecommunication or in the treatment of medical signals. However, ICA is not yet often used by statisticians.
All the best
Both methods are used to reduce the dimensionality of data into a smaller set of components. However, PCA utilizes the first and second moments of the measured data (hence relying heavily on Gaussian features) whereas ICA exploits inherently non-Gaussian features of the data and employs higher moments.
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