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What is the difference between whitening and PCA?

  1. Sep 12, 2012 #1
    Hi, all

    I am looking into whitening transformation. According to the definition and explaination of Wikipedia, whitening transformation is a decorrelating process and it can be done by eigenvalue decomposition (EVD).

    As far as I know, EVD is one of the solutions of principal component analysis (PCA). And the results of both whitening and PCA are uncorrelated(vectors, if the input are matrices). Thus I am being confused by these two methods.

    May I say that whitening is equivalent to PCA? If not, may I know why?

    Thank you very much for your kindly help.

    Best regards
  2. jcsd
  3. Sep 19, 2012 #2

    Matrix (M,N)*(M,1)=(M,M) = whitening is the passage (M,N)-> (M,M)

    The whitening transformation is a decorrelation method which transforms a set of random variables having the covariance matrix Σ into a set of new random variables whose covariance is aI, where a is a constant and I is the identity matrix. The new random variables are uncorrelated and all have variance 1.

    Standard PCA is often used for whitening because information can be optimally
    compressed in the mean-square error sense and some possible noise is filtered out. The
    PCA whitening matrix can be expressed in the form:

    where EDET = E{xxT }is the eigenvector decomposition of the covariance matrix of the
    (zero mean) data x, implying that D = diag [d1 ,d2 ,...,dM] is a M*M diagonal matrix
    containing the eigenvalues, and E = [c1 ,c2 ,...,cM] is an orthogonal N * M matrix
    having the eigenvectors as columns.
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