  We introduce the notion of Principal Component Analysis (PCA) of image
gradient orientations. As image data is typically noisy, but noise is
substantially different from Gaussian, traditional PCA of pixel intensities
very often fails to estimate reliably the low-dimensional subspace of a given
data population. We show that replacing intensities with gradient orientations
and the $\ell_2$ norm with a cosine-based distance measure offers, to some
extend, a remedy to this problem. Our scheme requires the eigen-decomposition
of a covariance matrix and is as computationally efficient as standard $\ell_2$
PCA. We demonstrate some of its favorable properties on robust subspace
estimation.
