What is the difference between principal component analysis and common factor analysis?
CFA analyzes only the reliable common variance of data, while PCA analyzes all the variance of data. PCA tends to increase factor loadings especially in a study with a small number of variables and/or low estimated communality. Thus, PCA is not appropriate for examining the structure of data.
Are PCA components orthogonal?
The PCA components are orthogonal to each other, while the NMF components are all non-negative and therefore constructs a non-orthogonal basis.
What is the difference between ICA and PCA?
Differences between ICA and PCA PCA removes correlations, but not higher order dependence ICA removes correlations and higher order dependence PCA: some components are more important than others (recall eigenvalues) ICA: all components are equally important PCA: vectors are orthogonal (recall eigenvectors of covariance matrix)
How do you find the basis of Ica analysis?
In ICA the basis you want to find is the one in which each vector is an independent component of your data, you can think of your data as a mix of signals and then the ICA basis will have a vector for each independent signal.
What is the difference between ICA and the central limit theorem?
One way, which is motivated by the central limit theorem, is to find the source space that maximizes the “non-gaussianity” of all sources, which can be measured in a few different ways. ICA is more of a class of blind Both techniques try to obtain new sources by linearly combining the original sources.
What is the first vector of the PCA basis?
The first vector of the PCA basis is the one that best explains the variability of your data (the principal direction) the second vector is the 2nd best explanation and must be orthogonal to the first one, etc.