Howtosignalprocessing
- What is kurtosis in ICA?
- How do you determine the number of components in ICA?
- What is the difference between PCA and ICA?
What is kurtosis in ICA?
ICA decomposes a multivariate signal into 'independent' components through 1. orthogonal rotation and 2. maximizing statistical independence between components in some way - one method used is to maximize non-gaussianity (kurtosis).
How do you determine the number of components in ICA?
U =eig(cov(D)); k= 31; sum(U( (end-k+1): end ))/sum(U); where U is the vector of eigenvalues of your sample's covariance matrix in reverse order, D is your data and k is the number of components you are using.
What is the difference between PCA and ICA?
PCA vs ICA
Specifically, PCA is often used to compress information i.e. dimensionality reduction. While ICA aims to separate information by transforming the input space into a maximally independent basis.
