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# Karhunen-Loeve Transform (KLT)

Now we consider the Karhunen-Loeve Transform (KLT) (also known as Hotelling Transform and Eigenvector Transform), which is closely related to the Principal Component Analysis (PCA) and widely used in data analysis in many fields.

Let be the eigenvector corresponding to the kth eigenvalue of the covariance matrix , i.e.,

or in matrix form:

As the covariance matrix is Hermitian (symmetric if is real), its eigenvector 's are orthogonal:

and we can construct an unitary (orthogonal if is real) matrix

satisfying

The eigenequations above can be combined to be expressed as:

or in matrix form:

Here is a diagonal matrix . Left multiplying on both sides, the covariance matrix can be diagonalized:

Now, given a signal vector , we can define a unitary (orthogonal if is real) Karhunen-Loeve Transform of as:

where the ith component of the transform vector is the projection of onto :

Left multiplying on both sides of the transform , we get the inverse transform:

We see that by this transform, the signal vector is now expressed in an N-dimensional space spanned by the N eigenvectors () as the basis vectors of the space.

Next: KLT Completely Decorrelates the Up: klt Previous: Covariance and Correlation
Ruye Wang 2016-04-06