A Linear Transportation $mathrm{L}^p$ Distance for Pattern Recognition

The transportation $mathrm{L}^p$ distance, denoted $mathrm{TL}^p$, has been proposed as a generalisation of Wasserstein $mathrm{W}^p$ distances motivated by the property that it can be applied directly to colour or multi-channelled images, as well as multivariate time-series without normalisation or mass constraints. These distances, as with $mathrm{W}^p$, are powerful tools in modelling data with spatial or temporal perturbations. However, their computational cost can make them infeasible to apply to even moderate pattern recognition tasks. We propose line