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.

@inproceedings{cavazza2016kercov,
  title={Kernelized Covariance for Action Recognition},
  author={Cavazza, Jacopo and Zunino, Andrea and San Biagio, Marco and Murino, Vittorio},
  booktitle={23rd International Conference on Pattern Recognition (ICPR'16)},
  year={2016},
  month={December},
  organization={IEEE}
}