Narsee Monjee Institute of Management Studies
If possible, with respect to signal processing.
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Jan 5, 2013
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This can be understood intuitively by considering the fact that a covariance matrix is nothing more than the square of a linear transformation matrix, i.e. a rotation and a scaling of the data. The rotation is defined by the eigenvectors, while the scale is defined by the eigenvalues.
A nice explanation of this concept can be found here: http://www.visiondummy.com/2014/04/geometric-interpretation-covariance-matrix/