  In this paper we aim at increasing the descriptive power of the covariance
matrix, limited in capturing linear mutual dependencies between variables only.
We present a rigorous and principled mathematical pipeline to recover the
kernel trick for computing the covariance matrix, enhancing it to model more
complex, non-linear relationships conveyed by the raw data. To this end, we
propose Kernelized-COV, which generalizes the original covariance
representation without compromising the efficiency of the computation. In the
experiments, we validate the proposed framework against many previous
approaches in the literature, scoring on par or superior with respect to the
state of the art on benchmark datasets for 3D action recognition.
