INTRODUCTION
The descriptive power of the covariance matrix is limited in capturing linear mutual dependencies between variables only. To solve this issue, 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. Our proposed encoding (Kernelized-COV) generalizes the original covariance representation without compromising the efficiency of the computation. Despite its broad generability, the aforementioned paper applied Kernelized-COV to 3D action recognition from MoCap data.
![](https://www.iit.it/documents/1452990/1465384/Activity+Recognition_01.png/c570bf59-af44-ed3d-8bfc-dc71f547a29d?t=1656081721651)